Hello everyone and hands up who likes performance and management says no school leader ever Here in England at least the culture performance Related pay Doing a similar job in another school being paid differently, etc There's lots of research. Well, there's a lot There's very little research to show performance related pay improves teachers and a lot of the work that I do today is Trump in that reforming traditional appraisal to a point where teachers are part of the process and So I'm going to put a resource on your screen. In fact, let me just put that up now A little kind of preview I want to talk about my new resource the five-minute research enquiry appraisal performance management plan Bit of a mouthful call it what what you want But it's a thought process for school leaders watching something that you can take and adapt to suit Your performance management policy to get to a place where your culture's right You can get teachers engaged in driving their own performance their own research enquiry. There's lots of Disclaimers that I'll unpick throughout this have to put this in position and how to make it work But I think with your performance related policy and the things that you're allowed legally out to do The things that you have to consider There's lots of factors. So let me just kind of you know, kind of set the scene I suppose 10 20 years thousands of teachers up and down the England at least always justifying the performance having to Submit three targets all the evidence etc. Etc loads of meetings whether you get them or not Providing reams and reams of evidence demonstrating how you've met targets. Um, there's lots of research all on my website I'm gonna go not gonna go through it But schools often ask how we can move away that From traditional performance related pay and there are at least on the Twitter bubble evidence that schools are doing this but beyond Twitter and There are schools that are not yet in that position and a lot of people asking me how So a lot of things coaching culture autonomy all these things that need to be in place Reform and lesson observations, etc. But this resource, let me just put it a little bit. What a larger for you I'm just gonna talk you through The methodology and I'll attach the resource To this video stream after I finished Publishing the live video and you can download and have a play and talk about it in your leadership team Maybe it's not too late. If you're watching here in the UK for August September You know, of course these things need to be ratified by governors But other schools that I work with different seasons of the academic year So I was working with a school in Brazil Recently their academic year starts January February. So not too late for different people to consider it But let me just put the first one up for you The first thing is a guess if you think of let's just assume this is in place. Here's the methodology For the individual teacher, I think the first thing they'll need to do is identify a focus or research focus What's urgent with their school priorities their department their team or themselves things that can be important I think with a line manager or a critical friend They would then identify a range of topics before I get into the slide and further. I'm just gonna pop to this slide Let me just put this one up it full size You've got here These are inspired by John Hattie's new meta-analysis database And These I've jumbled up So it's a bit like a little kind of retrieval practice quiz for you which have in evidence In terms of raising student outcomes, etc. And so on this template You've got your kind of blank fill in the gaps And then what I've got here is the actual answers now There's a lot more but these are the ones called to John Hattie's analysis where they are So you might find that this could be a steering point of course There's lots of others you can consider people watching here in England and evidence-based education and down Education and down with foundation, etc. Lots of sources, but you'll have your own school priorities and those should always be I Think a teacher's focus not just the professional needs But also their schools needs in terms of driving appraisal focus so we could select a couple of these I guess we pop back to my template here On this kind of step one identifying a focus. I think the next thing to do stage two is definitions When I started my doctoral research my supervisor straight away said My initial research inquiry question was what impact does social media have on policy? Straightaway I was asked to define social media define influence whose policy which you know England Brazil whoever What type of policy curriculum behavior, etc? So there's a lot of work just to do there in terms of definitions narrow narrow narrow the focus best piece of device I've ever been given And I'm gonna have to forget who gave it to me recently But it's really stuck with me is in terms of Research we should look rather than looking a mile wide and an inch deep Which is often what we do in the life of our teacher We should switch to a inch wide and go a mile deep And if we can have that as a research approach We're gonna we can dig deeper into the details. So it's really important to narrow the focus I guess here in terms of an impact framework that you could use part of your Research appraisal methodologies, of course, you'll need all the paperwork to support all these processes but I guess here we're looking for a mixture of You know these types of questions, what do you want to know in your research appraisal? What would you hope positive change to be like? What's current practice? What's best? Sorry, what's the best current practice and then according to who is an important question to ask How are you gonna collect the evidence? sustainability ethically Etc could also consider you might want to publish this piece of research in a teacher journal a school journal So you're gonna have to consider ethics in advance and anonymity permissions Etc and then how you're gonna disseminate best practice the schools that have CPD as part of the appraisal process throughout the academic year where teachers can dip in and out of different Kind of CPD experiences with their colleagues and externally and also regularly feedback on what they're doing tend to have a much more autonomous culture and so stage four You know once those things are in place you've obviously got your time frame to consider so key deadlines Where meetings would take place where CPD would take place? Traditionally, it's kind of three Meeting opportunities a year. We know that appraisal can only be good be as good as the people who lead it And I don't mean just the head teacher all middle all line managers who lead appraisal conversations They need to be equipped with the tools to have meaningful structured Conversations and this should happen on a weekly basis in line management meetings not just for appraisal windows So all that has to be factored in and we then have to think about research approaches Now there's a wide range of things that we can do for our own appraisal So let me just swap slides and go through some examples and I put out this is something I experienced in my own kind of doctoral research You know if you think about the kind of positivism and they're kind of The kind of Scientifically the off-stead type people tend to do the kind of positive approach Experiments gathering data all that type of stuff and then you've got this the subjectivism on the right-hand side Teacher experience in the classroom. I know that when I do this will tell you off or wink or whatever I'm doing It makes a difference to your behavior in the classroom. So that's subjectivism. How do I? Evidence and produce more robust data as part of research or part of an appraisal So you got this kind of different types here different themes They can be broken down in a bit more depth But you might want to conduct an experiment you might want to look at a case study or a piece of action research in Departments with students or in local schools. I think once you've got that Established you then have to think about how you're going to gather the data So there's a whole range of things that you can do here from observations to experiments Of course if you're doing this with students, you have to consider ethics, etc So I think once all these things are in place you have to also factor in how can this be made? Straightforward enough for a teacher to complete within an academic year For teachers watching here in England UPS, you know every couple of years. We've got the threshold Potentially there's an opportunity there for this to be adapted to suit a two or three year window However long it takes to go through that threshold Then we have to look at the data analysis So the testing the code and the counting and then starting to build up an evaluation And then if we look at all this on a page or suppose Got a little kind of question matrix format there This is the green cells here identify the verbal feedback project study I published with Mark Quinn at UCL A couple of years ago That's the methodology the 13 teachers use from seven different state skills verbal feedback research So you can see how different types of research methods have to be selected So back to the five-minute plan Again, it's a thought process and I am slightly taken a bit longer than five minutes to go through it And because it's quite an important topic the teacher then might want to pose their initial research question I Rather than just a limerger. I'm a big fan of the tuning protocol, which is a bit like a doctorate level PhD Aviva I'm at that stage where I need to go through my viva upgrade So I you know, obviously I'm working hard to refine my question narrow the definitions the focus etc But this is where we might want to pitch this and then present to my line manager the head of department a teacher from another Department as a critical friend and maybe a school leader And then what we're going to do also present our sample control the research control trial You know what we might want to do what we're going to consider Disregard along the way the interventions we're going to select again narrow that detail You've got your data collection period. So, you know, let's you know in England. We pose that question September, October We refine the details November December January February March. We are gathering the data Evaluate and reporting back through CPD in line management. It's a point where before we publish anything we refine our final research question in that middle of the academic year and This is threaded through CPD dissemination I've also read mentioned already we have to consider the ethical risks the safeguarding all those types of things and then Once we get to a place We're going to need to have some forms to support the teachers to evaluate to report And I think also if you think about how schools might fund Teachers with MAs or PhD studies or MP QSL or whatever it might be Maybe part of it could be that Maybe a hundred or two hundred words of your documentation that you submit to your school is made public Whether it's on a website or in a journal in a book a bit of a synopsis and Where we can report to governors, etc. Etc. So there's a lot more within this It's all explained in the full document But I just thought take a moment to share some of the thinking I haven't gone through everything There'll be one or two gaps here in this video But get in touch if you're interested for more I'm going to pin the resource Back on to the video once I finished and I hope it gives you some food for thought to think about how we can transform Traditional performance management and and and give teachers an opportunity to do things differently Become autonomous and drive that decision process for themselves But you know, we can't take a horse to water make them drink We have to also have the conditions for teachers to want to be able to do this for themselves also And so I'm going to stop there. Have a happy rest of your weekend Thanks for watching any questions. Send me a direct message by for now


Well, hello and welcome to Jonathan from the heart. I'm Jonathan Astley of Jonathan Astley.com And I'm so excited to be shooting this short video for you today. Our topic. Why do men go from hot to, wait, is that hot hot to cold? Mmm. Why do men do that? All right. Really quickly, if you're brand new to my YouTube channel, please hit the subscribe button, hit the bell so you can be notified of new videos. And if any time during this video this content resonates with you, please hit that like button. Lastly, these are my weekend videos. I shoot out on my balcony, very similar to the videos I shoot in my private group called Midlife Love Mastery. Check out the link below. This is a group where we have a webinar once a month and a private Facebook group. And if you ask me a question in the group, I'm going to shoot a personalized message just for you in the group. All right, let's talk about why men go from hot to cold. So we all know the merit. And by the way, I'm going to sound like I'm repeating myself if you've been following my videos for quite some time that men come on strong when they're attracted to someone, when they're feeling lost, when they're feeling limerence, and limerence is extreme infatuation. And it's very natural for a man and woman to bond closely together when they're feeling chemistry for one another, when they're feeling the pheromones and the hormones and the testosterone and the estrogen and the oxytocin bubbling together and makes two people want to come closer and closer and closer together. And then all of a sudden, boom, something changes. Okay. And I want to lean into this. Why does this happen? Well, let's first off understand that since the demographic I mostly speak to as midlife, which is after baby making years and before retirement, most people aren't on, most men aren't on the hunt for a wife and they're not on the hunt for starting making babies and starting a family with someone. I'm going to repeat that most men in their 40s, 50s, 60s, and beyond are not on the hunt for family. And that's one of the most predominant driving forces in our 20s and 30s. But when we get to our 40s, 50s, and 60s, oftentimes it's the hunt for simply connection with another human being.  And so one of the challenges that we're faced with is a lot of men in midlife don't know whether or not they want to be in relationships. So what happens after the attraction phase is all of a sudden they hit a phase of uncertainty, doubt, concern, concern for a couple of things. Are you the right one for him? But actually concern if he's even capable of going into deeper relationship. This is one of the reasons why when women hire me for my private coaching is to learn to vet for emotional maturity, to vet for compatibility. I even have an emotional aptitude test. I help you test men to see if they're in a good space to be in a relationship. Because oftentimes the reason why guys go cold isn't has anything to do with you.  Could there be chaos going on in his life? Could he be going through a nasty divorce? Could he have a contentious spouse? Could he have stuff going on at work? In these days, most people are feeling a tremendous amount of pressure in their professional lives. What about their children?  This is the anniversary of his passing. And I got to tell you that's left an emotional scar on me. It makes me wonder, can I actually lean into a relationship? Can I lean into being close to someone? Because this is a significant trauma. And I know many of you have experienced traumas as well. Not just childhood traumas, but adult traumas that makes many of us afraid of leaning in. So while we might want that companionship connection and physical intimacy, are we really capable of going further? This is why I'm such a big proponent of everybody on the planet of doing the inner work, doing the inner work to heal that foundation as a as a vaccination to emotional chaos.   And this was dedicated to Connor. Here's a picture of him right there. Can you see that? And I don't know if you can tell, but the shirt I'm wearing is the same shirt that he was wearing. Actually, this is a better picture of him. So just to show you that and it's the same shirt as you can tell. Because going back to thank you for allowing me to share that with you. I'm just wondering if I'll even get emotional today. I have a feeling this is going to be a hard day. And I'm just sharing an example of my own life. What about in your own life? What about in the man's life? When we're going through this period of uncertainty, when we're going through healing of traumas. And then a lot of people don't actually do the work to heal their traumas, which makes it very challenging to be in relationship with someone, to actually start to form a relationship with someone if they have unhealed wounds and traumas, whether they happen in childhood or they happen in adulthood. This is why I'm drawing attention to this. This why if you watch my channel, I refer to book after book after book after book. Because what's most important here is to understand that the fantasy we all grew up with, this whole fantasy that men are chivalrous and they're just going to claim you. And all you have to do is just sit back and lean back and just receive because that's all you need to do because men are going to know what they're doing. I can tell you if I didn't study this stuff on a regular basis, I would be incredibly clueless because I would be operating from the premise of simply connection and sex and companionship without any real destination. Because that is also one of the challenges of why men go hot and cold is they don't know whether or not they want a long-term relationship. They certainly know that they can enter into short-term relationships, but how many of them really want to go the distance? This is why, from your perspective, if that is what you want, if you want a relationship that goes the distance, then it's imperative on, imperative to express that early on. Let me repeat that, express that early on. Because men are actually somewhat, they actually do want to, I mean, good men want to protect your virtue.  And what that is is where he just starts dumping all of his problems, all of his concerns on you and basically says, this is why I can't lean into relationship. Let me repeat that, he's going to dump all his problems, all his concerns in his life on you and basically he's going to say, I'm not capable of being a relationship. This is why you have to vet for where he's at on his emotional level of his life. Is he in total chaos in his life or does he have a foundation underneath him that can build a relationship? Let me repeat that, does he have a foundation underneath him that can build a relationship? And what I'm really talking about is an emotional foundation and not just that he owns a home and pays his bills. Okay, those are all really great important things, but does he have the emotional foundation to build a relationship? This is why ladies, I am so critically, not critically, I'm so adamant about learning how to ask better questions from the very beginning, from the very beginning. But Jonathan, we're not supposed to interview someone on a date because it's just supposed to be about fun and let's just have fun. And you know what? Let's just focus on the fun. Ladies and gentlemen, the problem is when we're too focused on fun, we can find ourselves getting attached to the wrong person. And who wants to be with the person who's hot and cold, hot and cold, hot and cold?  What's that Jonathan? A threshold barrier? A threshold barrier is the first bump in the road in a relationship because relationships are going to have bumps in the road and when you get past the first one and you actually get past the first one and then there's going to be another one, most likely in another one in another one. And those build the deeper roots to a relationship. I'm going to repeat that. Getting through what's called threshold barriers, bumps in the road, little storms in the relationship. When you can get past one after another after another, that's going to build deeper roots of connection, of trust, of familiarity with one another. That is how a relationship is built ultimately. Now, ladies, if you've been following my channel, I don't have a copy of it today. I'm a big proponent of everybody ordering the book, Eight Dates by Dr. John and Julie Gottman. Eight Dates by Dr. John and Julie Gottman. Check out the link below to Jonathan recommend books. Why I'm recommending this book, by the way my coffee mug says, love yourself. Why I'm recommending this book is ahead of time, you're going to read this book and go, ah, I get it. I get what Jonathan means by the mechanics of a healthy happy relationship. And then what you're going to do is give a copy of the book to the man you're beginning to date.  Wait a minute, what, Jonathan? Yes, when you both decide you're going to explore a relationship together. Wait, what is that, Jonathan? You both decide you're going to explore a relationship together. In other words, be intentional. Say, are we going to explore being in a relationship together? If it's getting to the point of being sexually intimate, you have every right to say, are we going to explore a relationship together? And if he says yes, then you give him a copy of this book and you tell him the story behind the book, even give him a copy of this video and say, hey, let's do this together. Let's do this together to see if we're really a good fit for one another before we go down the intimate path. Now I know you're going to get a lot of flack and resistance, but here's the thing. The wrong guy is going to give you the flack and resistance. Let me repeat that. You only scare the wrong guy away, because when it's, by the way, one of the chapters in my book is called Speak Your Truth, Do It With Kindness. But then later in chapter nine, if it's sincere and from the heart, you can never say the wrong thing to the right person when you begin the process more intentional. By the way, I'm not suggesting you don't have fun and be flirty with one another and kiss one another and all those sorts of things. I'm talking about before it becomes physically intimate, be intentional. Be a little bit more pragmatic. Be a little less emotional and a little bit more. Does this really make sense for us to go down this road? Because guess what? By being intentional, by being a little bit more in your head rather than the emotional state of being because of the strong attraction, you can decide, is this really what we both want? Because let's face it, this whole notion that men don't know what they want. Well, that's partially true. They don't know what they want. But here's the thing, ladies, you've got to challenge these men. Stop giving them a pass if they don't know what they want. Start being intentional and say, look, if you want to be with me, this is what I require. This is my standard. But it's going to be very challenging if he's dealing with emotional wounds, emotional traumas, adult traumas, and he's not working on his stuff. Because I can tell you, I even work on my stuff, and it still makes it very challenging. And it makes it even doubly challenging for someone who is going through a lot of chaos, a lot of physical chaos in his life, emotional chaos in his life, whether it's a, like I said, an ex-spouse, his profession, maybe his children, whatever it is in his life, and just like what I've experienced. And most of all, I just want you to know, most men are good people. Most men are just good people. They're just bad daters. That doesn't make them bad people. And this is one of the reasons why men go from hot to cold. All right. I hope you found value in this video. I felt like I rambled. This was just, again, one of my weekend videos that I just wanted to share what was coming up. I'd like to hear your thoughts on this. Please post a comment below if this resonated with you. If there's something you'd like to share, I do my best to read everything. Also, like I said, if you'd like to join my group, check out the link to my VIP group called Midlife Love Mastery. And if you'd like some support learning how to vet men, check out the link to a free discovery call to see if working with a coach is right for you. All right. This is a good time to wrap up today. First off, I'm going to give myself a big, gigantic, Jonathan Bear hug of self-love. I'm going to reach into the camera and give you a hug of love if that's okay. I'm going to ask you to turn to someone, a pet, a teddy bear or pillow, or in my case, Connor's picture. Give it and them him a hug of love because hugs are a great source of love. Let's face it, we could all use more love in our lives. Thanks a bunch. Bye-bye now.


What's going on everybody got Cal and Shawn here relating sports cards. I've been three boxes for Chaiti Watt L.  Good luck. Good luck sir. Let's see we got two hobby wonderlite. Good luck. Good luck Let's see what we got super fracter red Come on. Maybe a little brawn and brawny action. He ain't gonna tell him. Okay. Why are we telling the weather then? Refractor first of Aaron Bradshaw. Interesting. Okay And that was the most embarrassing. Yeah, that was not good questions.  Yeah, how do you gonna react to the weather? There is not worried about it the next seconds. We got a walk in the next question There's a goal. It's gonna be Santiago Vescovi point guard for Tennessee This one there go wave 250 or go lava. He's Vescovi Yeah, yeah, Todd was Todd was being nice 399 Bryce Hawkins Providence his face said it all though. He's like, yeah, yeah, like you serious Talking to me messin with me right now. He's talking to me to 99. We got Aliyah Edwards nice been there for Yukon and That's gonna be a refractor simian Wiltshire St. John's Tyler Kolek Just an insert Kyle filipowski Filipowski I'm about the Conrad Filipowski Lasik Center. Okay. I was just thinking it Yeah, Conrad Filipowski. Yeah, there's a Zach Eadie This one there is a big man for Purdue to 150 Zach Eadie and refractor first Ray John Ellis Edie Caleb love Caleb love Cameron Brink Get nothing else in the queue guys. We want to do something right after this Get it going right away. There's a extractor first Chris Johnson Tyson Chapman Reed Shepard there on the first aqua. I want is to one two ninety nine. That's when they're young Kentucky blood Nice read shepherd Really? All right Zachary Let's take them down then there's a extractor of Zachary reassure the Frenchman one of the top picks in this draft Another international French guy coming in we will I'm actually two of them supposed to be the top five There's a Marco Jackson for Kansas refractor Playing some good ball in France Good ball Fractor first Garvey dual Justin Edwards for Kentucky Darren Green Refractor first Tayson Chapman Tyson Chapman to one two fifty refractor there for Ohio State First McKinley, that's always funny when someone's stuck behind a little bit Yeah, I figure not if they're live or not There's a wave Cody Williams first rounder there out of Colorado to two ninety nine Yeah, I think so Lane. I think so The seven foot guys that can only dribble. There's a sharp shooter Jair McCain for Duke Reed Shepard Xavier Booker Michigan State Oh, how we doing sir, how we doing? There's a Caleb love on the refractor hey, Arizona Hey, appreciate that MOOC Appreciate that sir. There's a brought in James and good luck again. Let's do it. Let's run it right back We're on it right back Ex-fractor first Jamal shed and gold Cormac Ryan first first gold wave for UNC former former Notre Dame player Purple wave is going to be Brandon Garrison. Let's go Carl. That sounds nice There's a Katelyn Clark on the insert there Iowa All right, that'll be one box down we'll do another hobby and then another delights Keep on rolling him. There's still nothing in the queue after this if anybody wants to get something going Do it right after about only five minutes or so to have to go grab a box or two There's a Omaha billu. I just went there on the aqua to two ninety nine first for Iowa State Omaha It's a Katelyn Clark face Blue is Lane blocker to 199 blue a ray wave Arkansas and sharp shooter McKinsey and Bako Kwame Evans insert refractor first Jaden Acons, Michigan State And insert is Zachary for the international spot France on the brawn and brawnie. Where you at? So 99 we got Quentin post first uh first green up We can post that at bc and Rob Dillingham nice one there to Kentucky The uh point card at two ninety nine Man's got some speed He's like uh They like the Aaron Fox There's a purple wave Justin Edwards Eh Some people would say that Spencer. I I assume I guess some people might say that I I wouldn't There's a Riley Cougal insert there There is a game at uh in Detroit though. That is a thing refractor first Sean Stewart Barley cannons inserts the phone castle. There we go There's Sean Go uh-oh skydiving's here Skydiving's here. There's a extractor first of Boba Miller. Hope he's practicing.  He's probably on the game right now Getting some dubs I have to imagine There's Wooga on the first for the Kings Wooga to 399 out of uh Miami Sharp treaters Ron Holland g-league and Tyrese Proctor insert Tyrese First Quentin post bc Practice in a bit gats a shame. Let's go. There's a Riley Cougal insert Let's go Shane Try to catch a couple gums on your ad church True and gold there's kylan Boswell for Arizona Nice one there point guard for zone on the true goal first auto Kylan Boswell So I got some more uh bowman's best you for avan coming up here next. We'll get that going here Armando Baycott X Fractor who card prepping Mookie cook Let's go lame PSA order coming I like it. There's a first H.A. Hoggard for sparty One's Rob Dillingham Kentucky Stefan Castle X Fractor of Baycott UNC last pack of the hobby We'll get the delight There's a Tyler Burton to 150 first for Nova Bryce Hawkins Now for delight Good luck. Good luck. Yeah. We'll get Evan going after this We've got a kylan Boswell that one's a 299 aqua wave for zona To 399 Namari Burnett out of michigan Got to 199 jimmy or young nice one there of uh maryland You know prospects and to 250 is rj jones And orange is going to be cormac ryan again for UNC cormac ryan And a yellow on the backs Tyson Chapman Ohio State Refractor first Aaron Bradshaw got Caleb love Garry dual And brawny james that's going to be the short print brawny Nice one there for you uh chai diwak Any short print bonnie james very nice Take that and then a rob dillingham right behind the first auto for kentucky Some good back-to-back cards there point card for kentucky to 250 Really nice one there back to back Got a spencer jones on the first base auto first um stanford And on the back we have an angel reese insert auto for ls u One is 90 out of 99 on the throwback auto Really nice box there on the delight Alrighty Alrighty and that'll do it for you on the three boxes have one delight and two hobby omen chrome uh u basketball Some really nice ones there, sir. We'll get that right out to you


Shagu Shoumi marched the governorship candidate of the PDP in Ugun State. Shoumi polled 554 votes to the vet, his closest rival, Jimmy Lawal, who scored 30 votes and horrible Ladi Adibutu with 15 votes. The election, which was peaceful, free and fair, was conducted with security and monitored by INEC officials with members of the Interparty Advisory Council of Nigeria. Shagu Shoumi, in his acceptance speech, commanded members of the party for their support and promised not to let them down. They called on other aspirants to work together with him to secure victory for the party in the 2023 general elections. Otuba Adibutu polled the total number of 15 votes. Shagu and Tony Shoumi polled the total number of 555 votes. Hello, hope you enjoyed the news, please do subscribe to our YouTube channel and don't forget to hit the notification button so you get notified about fresh news updates.


Some Republican and conservative political and media influencers based their proposals for section 230 reform on the idea that conservative content is censored by social media platforms. Researchers have taken this accusation really seriously. But the research consistently shows that conservative content far from being suppressed consistently earns more interaction than left leaning or even politically neutral pages or accounts. In fact, a small number of conservative users routinely get more conversation and engagement than their liberal counterpoints or even traditional news outlets. Facebook's own data shows that the top performing link posts week after week are generally from conservative voices. And in fact, some tweaks that Facebook made to its newsfeed algorithm in 2018 had more of an effect on cutting exposure for progressive news outlets, then it did for conservative ones. So there may be good reasons to reform or update section 230, but conservative bias isn't one of them.


Well good morning friends. I just woke up like maybe a couple of hours ago. I don't know what time is it? Almost nine. So anyway, I just sat down to film a few videos and there's a lot of carnage currently. Do you see this? Oopsie. Uh videos will be coming up soon. So yeah and uh also So, this is more carnage. Planner carnage everyone. So now I am gonna run upstairs. Hold on, do I have all the things I need? Yes, I do. Um, I am going to run upstairs and import this footage and possibly get a video or two edited because I just filmed three videos and Doyle is on the sofa judging. He like cried during the videos. He's like, Mom, I want to go and get the attention. I'm like, no, no attention for you. He got a lot of attention this morning. He's fine. So anyway, back into the office with my new light. Woohoo, but I don't need, I don't need my light right now because it's very bright in here and the window isn't even open yet. So wow, that's bright. Okay, so I am gonna say goodbye for the moment but I am going to work a little bit if Doyle will allow me and uh I'm also ready for the simply gilded release. So I actually have it on my phone. So I'm gonna get uh an alarm alert when it's live So that's I think at 11. So it's in a couple of hours But yeah, so that's exciting. There's a couple of um washes that I really really want Which is weird because I don't like bows and y'all know that but I like most everything else. So time to treat yourself Time to treat yourself Anyway, all right. I'm gonna stop stalling and I will see you guys in a little bit Well, hey, I am still in the office, but I have moved around. I promise Doyle is uh behind me sleeping Doing his thing, but I've had a very productive day. Um, I've edited several videos. I've actually posted Today three videos. So I know you guys will probably like stop posting, but It's just what's gonna happen guys. It's just happening I actually just watched What is that Netflix show with Ali Wong the always be my maybe it's kind of cute Um, and I I did that as well as at the same time Fulfilled the rest of these little giveaway packs. So Actually My elbow out. Okay. So I actually um have gotten several messages from a few of you guys And I have some packages ready to go, but clearly I have more So um, if you guys didn't see that vlog, I think it was a few days back But if you all are interested in Some of this stuff. Um, if you have a local planner meetup in the us, you have to have a us address Um, just send me and like my email is down below. So send me an email With a us address as well as like, you know If you have a facebook group or an instagram page for your planner group put that in there too So i'm not just gonna like send it to somebody that just wants a bunch of washi And rather give it to like a group or something. So anyway, um, they are Most of them have like two washi's inside has my little logo on it There are some that have three because some of them are Like a little like thinner like this So it's like there's three of them in that one. So anyway, but that's that's what I've been doing all day And uh getting stuff scheduled for the week in the youtube channel and all of that good stuff So anyway, my battery is flashing. I'm gonna say goodbye here because I'm really not doing Too much and I want to get this video up and ready for tomorrow and it is about seven o'clock right now So yeah, but um, yeah had a productive day at home and Excited for tomorrow. I'm going to the planner meetup. So I'll be doing that. I'll vlog a little bit I usually don't vlog much When I go because you know like other people you just never know if people are cool with being on camera I totally get it. So anyway, thank you for hanging out with me today I really appreciate it. If you like the video give it a thumbs up, but don't forget to subscribe and I will see y'all tomorrow. Bye Well folks it's time to kick it old school


Can PK make the Congress okay? That's the question we are asking today because Prashant Kishore is going to be part of the Congress's campaign for 2024. And he's had a very good record in pushing parties beyond that victory line. And he did that with Narendra Modi in 2014. He did that with the miraculous alliance between Lalu Yadav and Nitish Kumar, two old foes who came together to win Bihar in 2015. He did that with the Congress in Punjab, failed in UP in 2017, but he did an amazing job. Lot of credit has been given to him for Mamata Banerjee's recent win in West Bengal, where the BJP was almost at the door knocking on the door. Many people said it could take over West Bengal, but Prashant Kishore strategized, worked out the messaging, worked out the micro details of that campaign and Mamata Banerjee won. He made a presentation to the Congress Party last year and there was a lot of speculation that he might either join or take over the Congress Party's assembly campaign for Goa Punjab, UP and the ones which recently went to elections. That didn't work out, they parted ways. Now there's talk that he's coming back and there's a presentation that's doing the rounds amongst journalists, which is supposedly Prashant Kishore's 2021 presentation to the Congress Party, to Sonia Gandhi and Rahul Gandhi. I cannot vouch for its authenticity, but people who have covered the Congress Party for a long time know Prashant Kishore, tell me that this is authentic and I got a copy of it. The core thing which you already probably know is that Prashant Kishore recommends that there has to be a non-Gandhi working president of the Congress Party. What he means is that if Sonia Gandhi continues to be the president of the Congress Party, there at least has to be a non-Gandhi working president who directly reports to her, but has all the authority to deal with everyone else and the two other Gandhis, Rahul Gandhi and Priyanka Gandhi, actually don't deal with the nitty gritties of the Party at all. Rahul Gandhi recommends becomes the head of the parliamentary board, which as you know doesn't have many people, just a few MPs in Lok Sabha and Rajya Sabha. So he keeps there, but he also is one of the faces for the Congress's campaign. And there's Priyanka Gandhi, another face for the Congress campaign, who would head the Coordination Committee, which would coordinate between the Party leadership, the Working Committee and all Congress workers. That is the role he has for the Gandhis. The Gandhis are there because they're brands, I'm assuming that's the reason, and because they cannot be removed that easily, but he's delegating parts of it and pushing a non-Gandhi into the top leadership of the Congress Party. In fact, he has in that presentation, he says that for most of the Congress Party's history, till the emergence of Indira Gandhi, there were Congress presidents changed regularly every two to three years. Since then it has not happened. So that is one key thing he says, he has two suggestions that even the president can be a non-Gandhi, but he says that that is very difficult to achieve. Its impact is very high, something which is relatively easier to achieve, but the impact is moderate, is to have a non-Gandhi as a working president of the Congress Party and a non-Gandhi as the chairperson of the UPA or the Coordination with other parties. So those are the key things that you get. Along with that, he says that the Congress organization has to be completely overwhelmed. It is essentially dominated. The leadership is dominated by people who have not won elections, who either lost it and have been nominated to the Rajya Sabha or simply haven't even fought elections for years. So he says that they have to go. There has to be one party, one ticket rule, one family, one ticket rule in the Congress Party, which ensures that people from established Congress families don't automatically get tickets to fight elections. And even if they lose, then they end up in the Congress Working Committee or some Congress post. So he's essentially saying there has to be direct elections for at least 50% of the office bearers. The organization has to be expanded dramatically. Elections have to be held. Those who win elections have to be brought in. The organization itself should be in a way representative of the demographics of the country. So if 50% of voters are women or more are women, then there has to be that many in the representatives in the organization itself. It should represent the youth, the poor, SC, ST minorities exactly or more or less in the way in which the demographics of the country is, according to the census of India. Now, these are things that are difficult to achieve, but obviously he's saying that this will take time, but it has to be done. This is one of the key parts of his suggestion. Now, he says the Congress has to target about 40 to 45% of the vote shed to come to power. And that means about 30 crore votes. Where will those 30 crore votes come from? And there are two tables where you can see that most of it he expects about, I would say 55 to 60% of those 30 crore votes he expects to come from minorities, from Dalits and from Adivasis. That's what is the core of the Congress party's catchment area, which he believes from where the votes will come. And as you go up the ladder, if you see upper cast and OBCs, Prashant Kishore does not expect much support for the Congress party. In terms of income groups, he expects a bulk, again 55 to 60% of the votes to come from the very poor, the poor, and the lower middle class. And again, even when you come to the lower middle class, the proportion of votes that he expects is actually lower than the votes that he expects overall. So bulk of the votes, again, he expects to come from the poor and the very poor who earn anywhere between 100 to 200, less than 100 to 250 rupees per day. That's the catchment area again. And he acknowledges that as you go up the ladder, whether it's the middle class, whether it's the upper middle class and the rich, the Congress party actually cannot expect to do, get more than 20, 25% of the vote in that space. Now, other than that, he also identifies eight core catchment areas for demographic zones from which the Congress needs to get most of its votes. Number one is women. He identifies women as the strongest group in which the Congress can get votes. Other than that, he identifies landless laborers, farmers, the urban poor, the middle class as well, and the youth, the unemployed youth. In fact, if you look at the breakup he makes between the age group, age group-wise, breakup he makes as to what the Congress can expect in terms of votes, most of it is amongst older people or younger people between 18 to 25. The 25 to 35 age group he more or less assumes the Congress cannot get. This is an interesting point because he assumes that there is a degree of angst amongst the younger lot who are unemployed, which the Congress can tap into and get votes. Now, how do you target these eight core groups? You do it with targeted ways of messaging and reaching them.  By messenger, he means that you need to find some charismatic people who are icons who will speak to those specific target groups, women who are popular amongst women, Dalit leaders or popular amongst Dalit leaders, rural landless laborers. There might be people who landless laborers look up to. So take these icons who are opposed to the government or opposed to the NDA, who you have to identify and use as iconic charismatic messengers. The message itself, the messaging itself has to be targeted to each group and each locality, micro messaging in detail. Then comes the machinery. The machinery means that you need a large group of foot soldiers. You need a big digital footprint to convert that message into votes. And finally, you need a mechanism. That mechanism essentially is the entire system through which all of these will be coordinated and the message, the outreach will be converted into final electoral victories. Now Prashant Kishore recognizes that the Congress has very little chance of getting backing from the mainstream traditional media that he says is more or less controlled by the government and by the BJP. So very little chance of making any inroads there. But he does point out that even there the Congress does not actually look at data or science to do its messaging. It does it at the wrong time. It puts all its resources in the last few weeks where it's too late to convert people to go and vote for the Congress at the voting booth. So he says that it has to be essentially the digital platforms which the Congress has to rely on. It has to create an ecosystem of YouTube channels, digital networks. It has to depend on Twitter, Facebook, Instagram and other social media. It has to create WhatsApp groups in every single village, a single WhatsApp group, integrated, vertically integrated in every single village. People have to be reached through phone calls, I'm assuming, because there is an entire table put out which says that so many users to be reached through their phones. So there is that digital outreach which he's talking about which has to be done because the Congress does not have space in traditional media or even in television. And one of the ways in which to do that he's saying is to use local comedians, stand-up comedians who people watch, use them, those who are ideologically aligned to the Congress party, use that workout memes, create tweets, social media posts and lots of articles, lots of publications which are aligned, local publications which might be aligned to the Congress that those have to be tapped. He also gives you outdoor messaging systems that are important like for instance hoardings, all paintings, things like that. He says that the Congress has to put out 1 crore square feet of hoardings to publicize its message and this is what brings me to the great big hole in this presentation and Prashant Kishore's, at least its public presentation that we are getting to see, that we are getting to know which is I called up a few people to find out how much it costs to put up a hoarding in Delhi for instance. And it worked out to about 1250 rupees per square foot per week. Now think about it, Delhi is obviously very expensive, smaller towns will be one-tenth that, Mumbai might be more, the metros will be somewhere between 70 to 80 percent of what Delhi is. So I would say that one can assume that the average would come to about one-fifth of Delhi's cost. Now imagine 1 crore square feet for one week at 250 rupees, that's 250 crore rupees for just one week of hoardings, 1 crore square feet of hoardings. Now Prashant Kishore says that there should be 5 crore Congress members out of which 2.5 crores will be active members. Now let us assume that each active member has to be given 10 rupees per day, which is nothing for their own expenses, which means that they still have to spend a lot of money out of their own pocket. So 2.5 crore people have to be given just 10 rupees to maybe fill a part of their petrol, some amount of tea, something that they are going at a tea stall, they are buying tea for everyone. 10 rupees per day for 2.5 crore people is 25 crore rupees a day over 2 years, 25 crore rupees a day for over 2 years for just your active worker. Forget about the hoardings, forget about your costs of putting up, bringing stand-up comedians to do things, putting up YouTube channels, your cost of your messaging, the cost of organizing all these yatra's, adivations, door-to-door camping, forget all of that. I am just saying this little bit is going to cost 25 crores a day if it is just 10 rupees per person, per active Congress worker. Prashant Kishore has a very interesting idea to counter the government's freebies, which are closely associated with the Prime Minister's persona. He says that each Congress worker needs to do 5 acts of kindness per year, per out to 5 families around them. So with that, they will be able to reach 12.5 crore families, 2.5 crore workers, Congress workers reaching 12.5 crore families and Prashant Kishore right at the beginning tells us, and again I am saying Prashant Kishore, this is a presentation which could be Prashant Kishore's, he might deny that this was his in the first place. He says that there are 3.2 voters on an average per household, so essentially you reach 40 crore people out of which you have to, these are direct door-to-door reach, outreach through kindness. So what does that kindness mean, that's not spelt out, but that kindness would probably involve some amount of spending. So I am not even looking at that 40 crore people to be reached through some kindness in the year, I am not even looking at what that cost would be to do a simple bit of act of kindness. Some of it would be free, but some of it would cost some money and therefore this is the key big hole that Prashant Kishore's presentation had, where will the money come from? The Congress party doesn't currently have money. It will need anywhere between 20,000 crore to 25,000 crore to fight the elections on that scale that Prashant Kishore is asking for. Even if I am conservative, at least 10,000 crore. It declared in Prashant Kishore's own presentation, it says that the Congress declared about 918 crore of income in 2018-19. So even if that has gone up and we have no reason to believe that, there is no clear plan where the Congress will get the money from to implement this vision. How will it do it? Where will it get the people for all those foot soldiers? Why would people turn to the Congress? Yes, the leadership can go ahead and try it, but if the leadership is already disconnected from the people, how can they convert and go to people and convert them towards the Congress? So there are these holes in Congress, Prashant Kishore's presentation, which are the serious issues that Prashant Kishore will have to explain to the Congress party and perhaps some of it is not going to be in a presentation. Some of it will probably explain off the record that how do you raise money? Because after all, Prashant Kishore took the Trinimal Congress to Goa and fought elections. Some amount of money was used in Resbingol and in Goa, where the Trinimal Congress in Goa had virtually no chance, but it managed to convert a few people to its call. So there has to be a way for the Congress to raise those funds. Prashant Kishore's own admission says that the rich will not vote for the Congress, the upper middle class will not vote for the Congress, the upper class will not vote for the Congress. So it seems that that hole is difficult to fill because the money lies with these people. Why would they give that to the Congress? That's the question that Prashant Kishore and the Congress needs to answer. That's the show today. Keep watching NewsClick, like us, subscribe to us and do share this video.


There's a new metric that Google added, right? Or I guess the Securities Exchange Commission has added, or whatever the central body is that has added this. They added a new metric to stocks. And the new metric is carbon footprint. You know, if someone, I believe it was carbon footprint, Cheryl, correct me if I'm wrong, I forget. And it was basically grading these companies in terms of if they're good for the environment or how much damage they do to the environment. They gave Walmart a B, literally. They gave Walmart a B. You know, I'm not a teacher. I don't know if you're a teacher, but if you had a classroom with 30 people, 30 companies, right, and you were grading them of what their carbon footprint is, how well they treat the environment, would you give Walmart a B? Who would be failing? I don't know, a company that blows up nuclear weapons around the world every month? Like, Walmart gets a B? That's central power for you, right? And people will read this, investors and stuff. And they do this, by the way, because hedge funds, they're gonna introduce some kind of metric saying, some kind of rule and stuff saying, oh, these funds can only invest in companies that have a B rating for their carbon footprint. And then the funds will go, okay, right on, we can invest in it. So they're only doing this, so certain companies will be eligible for certain funds to be put in there, right? The worst thing us human beings, humanity can do for the environment is give central power more power. There's no doubt about it in my mind, okay?


Če, da se ima. Če, da se ima, ešte, da se ima, me rečimo, da se vse sredne kažem, več, da se je pričočilo, mi se pričočilo, da je pričočilo. Zelo bomo pačo, da se nekaj tačimo, da je bilo pojavno, da je pravi, tudi na socialne netvore. Tudi vič je odvenila klavnje, vljut, datacenske vče, pri Sam Francisco. Proto, da se počuče biti o zelo kratiljšku. Sve, da so kratiljški, nekako ne zelo, da je to ovo. Kratiljšč je tudi socijalne občajstvo. Kaj je zelo kratiljški, ki je zelo kratiljška, vzelo kratiljški v zelo, kaj je zelo kratiljška. Vzelo kratiljški vzelo, da je tudi vzelo, that we adjust into our data pipeline. Then the data is kind of normalized and various features are extracted. And then we calculate cloud score as well as various topical sets. So for example, for a given user, we calculate what is this user interested in, what is this user expert in, and so on. We score about 750 million users with the cloud scores and assign topics to about 400 million users, so pretty large data set. On top of this, what we also do is when the user comes to cloud.com, we offer some content that user can share. And then on top of that, we can kind of suggest user what is the optimal content to share so he gets maximum engagement with his audience, as well as we offer kind of suggesting, hey, these are maybe the optimal times that you can post in order to maximize your engagement. So here is an example of how cloud.com looks, if you come in. So you can see your profile page, your cloud score. Then there is maybe a topic that you just selected or there is topics of your expertise. We show some experts and also kind of some data regarding how frequently the talk was talked about. And then if you decide to share some content, you can go, basically you get into the sharing mode and then among the things you can kind of go and select, hey, I want to actually not share now, I want to schedule it, and then you can figure out what is the optimal time to post it. And feel free also to interrupt if you have any questions during the talk. So what is the motivation behind this? So for a lot of power users, a lot of brands trying to reach their consumers or maybe just some kind of celebrities that try to engage with the fans, often time objective is when they post something, they want to get the maximum response possible. So basically by posting at the right time where we are trying to argue that actually you can increase your engagement. And that's what this research is about. And in the last couple of years, if you look at it, there has been like tons of infographic studies where basically they try to kind of recommend you, hey, what's the optimal time to schedule. And a lot of these studies did recognize that actually if you go like from network to network, different time, same time doesn't work. Some of the studies also recognize, hey, if you're in New York, different roles apply than if you're like in Europe. But a lot of these studies were quite anecdotal. And then look at the problem in a very systematic way. So here what we are trying to do is basically introduce the problem to academia, open data set, and also kind of get a little bit more systematic approach in solving the problem. So what are some of the challenges when tackling this problem? The graphic studies, a lot of them go and analyze data. Hey, for a given city, for a given network, you do some aggregate studies. And that's pretty doable, because once you have a lot of people in the city, you can get a lot of good aggregate stats on that level. But when you start talking about what are the optimal time for individual users, things get a little bit trickier. So let's say you're going to assume that your time is cyclical, the user behavior patterns repeat week to week, and that you want to maybe study your problem in 15-minute granularity. So even just like trying to constrain problem in such manner, you're going to have about 700 time buckets for a week. And then to start getting some significant statistics for each of these buckets, actually for individual users, it starts to be quite difficult. On the other hand, also there is no open data sets regarding this problem. And then oftentimes the challenge of the problem comes, because all of us are pretty unique. All of us have unique audiences. So if you live in New York, you probably don't have an audience that is only located in New York. In this example, for example, for a certain user, you can see that 43% of his users are in US. And even US is like a three different time zones in different cities.  So trying to tune in, to create optimal times when to post for this kind of complexity of audiences is not as easy task. And then on top of this, each network needs to be observed separately, because simply different behavior patterns and network dynamics are exhibited on different networks. So what is the kind of problem setting for a user on a social network? We want to find the best time to post a message in order to maximize the probability of receiving the audience reaction. Yeah. So the question is what taxonomy of the social networks use. So this study is done like on Twitter and Facebook separately. And we do a little bit of data analysis on other networks, but not to calculate the personalized schedule. Yeah. And so yeah. So this is the problem. And then like some of the constraints, some of the kind of limitations we put when approaching this problem, basically we consider replies, retweets, favors, likes and comments. We assume that users exhibit like weekly behavior patterns. We observe first 24 hours of reactions. And we'll show later that actually like once you post something, the probability of receiving reaction steeply decays with the time. So first 24 hours seem to be plenty of time. And we observe the problem with 15-minute resolution. And starting bucket is basically from the when the Monday starts. So first 15 minutes of the Monday. So overall the system overview. So when the user comes to cloud, he registers his social accounts, then like on the users we have, we collect the data, we fetch them from external networks using their social network APIs. We parse the data, we parse them in normalized form. So all of the networks, we have like a different JSONs or like a different data structures, but once they kind of get at the end of this system, they are all represented in same like a kind of proto buffer form where we basically capture who is like what is the post, what are the reactions and all kind of the metadata that goes with them. From the collected data then we derive a couple of building blocks. Some of the most basic building blocks is the application profile. So for a given user, when does he usually create content throughout the week. The second one is reaction profile. For a given user, when does he usually react and then post to reaction profile is basically it's kind of probability function of getting reaction as a function of time given that you created the post at time zero. And then the user input graph and user output graph is combined to derive like second degree schedule and first degree schedule. And then we kind of just prepare data and serve it on cloud.com. And our pipeline mainly runs in a hive. It mainly runs in a hive and then like if you need to do a lot of kind of transporting functions or like a machine learning in Java and then basically wrap them up in hive UDFs. So this way we can do still kind of complex things in Java through UDFs and then like how solve the problem where you have to kind of deal like with a lot of different data sources and do a lot of joins and basically maintain kind of data transformation complexity. So let's now get a little bit intuition about the audience behavior. So one of the most important things that characterizes kind of the social networks is a post reaction time. So here for example we can see like a first 24 hours of basically since the post was created and on the axis and on the y axis we can see like fraction of reactions received in the first 24 hours. So like when it hits 24 hours like it's one. And here we can see like that actually different networks exhibit different patterns. We can see that for a Twitter roughly like first 50% of all reactions is received within first 24 minutes. So basically it's very fast network and then for the Facebook to reach first 50% it takes about like a 4 times longer. And then other networks are kind of more similar to the Facebook basically. And why is the post reaction particularly useful in this study? Well it's useful because we use it to anticipate the reactions that user will receive if he posted this moment. So if you apply basically discrete convolution of if you post a time t you can get like what is anticipated amount of reaction you will receive throughout the time. So like post reaction in terms of some other kind of looking it through the lens of some other dimensions. So in this case what we did like we look at the post reaction profiles for different topics. So if you post let's say some content on the topics or politics like does that get like more reactions faster than food, drink or like a passion. And there seem to be like some difference like not as pronounced as in the case of where we are dealing with different networks but it's also something that it may be like our worth considering. And also we look at the post reaction profiles as a function of how many in degrees like user has. So for example how many followers certain user has and it looks like basically if user has about like 100 to 10,000 followers basically the reaction profiles look very similar. But then if the user is kind of getting in the cohort of 10 or like a kind of celebrity level where you have more than a million followers it seems like reaction profiles are quite different from average users. And this may be just because maybe the celebrities and guys with less followers are using social networks less and basically they are not reacting as much. So if you elaborate your primary objective it's the kind of broadcast more than react. And then if you are like a user, like just a casual user maybe you are just browsing or rarely even logged in in the social network. So another part of the audience behavior we do here is look at how the reaction behavior changes our data set for users from New York and we look at user reaction behavior across Twitter and Facebook. So basically here on the x-axis we have like a week and the working hours are kind of showing a little bit in the blueish and on the y-axis we have the reaction probability of the audience. So we can see that Twitter and Facebook do exhibit quite a different behavior. Twitter shows more prominent pics and while the Facebook usage is more steady throughout the week. So this is basically users that connect to cloud and then basically they are like a Facebook account. And we don't distinguish between if they use like a Facebook via mobile client or the web browser. But that's also a good point. Actually that's a great point because one thing that we can notice on the Twitter, these secondary pics which occur during the commuting hours. So I would imagine people are actually going on their mobile phones and checking out before they catch bars or something. And then also it seems use it just kind of drops out on the weekends in general. And on top of this we kind of do a little bit of analysis of correlation and similarity of audience behavior for the same users, for his audience on one network and the other network. So basically for example we can notice that correlation between audiences across two networks is always kind of they are positively correlated but correlation is pretty homogeneous from zero to one. So the similarity of the audience behavior peaks around 0.7. So this tells us that actually the audience behaviors are somewhat correlated and similar but it is actually if you want to post at the optimal time you maybe cannot reuse schedule that you calculated for Twitter to post it on Facebook. So you need to kind of be network specific. And then basically the way we do it like we calculate for a given user we can calculate his audience reaction behavior by aggregating all of his let's say followers all of his audience's reactions so we can look at when all of his audience reacts and that basically gives us like a vector which is mapping time bucket towards the value. And then for a given user that like the vector that we calculate for the Facebook and Twitter and perform correlation similarity on this and this is basically distribution of those correlations and similarity. Yeah, yeah. So for us we combine them like saying it's basically the weight is one like reaction is reaction but you're right maybe like different reactions if you're let's say brand and you want to engage with the customers and you care more about comments maybe you're actually looking to optimize on the time when people are more willing to comment than just before going to sleep clicking on likes. That's a good point. So basically let's look a little bit of audience behavior differences for different locations. So here we kind of pick couple of cities around the world. San Francisco, New York, London, Paris and Tokyo and look at the audience behavior differences for Twitter and Facebook. And we can notice that actually San Francisco and New York like U.S. cities do exhibit like a similar behavior like Tokyo is a little bit off where you can see that actually Facebook usage picks up off hours and also it's interesting to note that comparing in New York and San Francisco you can see that actually San Franciscans tend to wake up earlier and I try to kind of look for some validation of this and being before like did some studies where they actually kind of claim that yeah, San Franciscans do wake up about like 50 minutes before New Yorkers. So it looks like actually like for a given city like there is a lot of factors that one has to take in account and they are like basically connected to the working hours, to commute hours, to lifestyles and it's it would be very hard to kind of build a model that generalizes well across the cities, right? So once you're kind of doing this stuff you really need to kind of dig and kind of look at the stuff look at the schedules that are personalized not like a globalized per city and then so this is similar just looking kind of audience behavior similarity and the correlations within the same city like do users even within the same city exhibit like similar behavior patterns and even there we get that actually kind of similarities for example for New York we just got a little bit about about 80 about 0.8 for Twitter and 0.8 again on the Facebook and then correlations within the cities they are highly correlated like the curves are kind of very shifted towards the one but still depending for the audience there are some guys which are less correlated and within the same city audience behaviors are quite different and then we kind of just for the fun look at the audience behavior in respect of the topics, right? So the idea was like maybe some topics actually get many more reactions in some different times but for topics like some slight difference can be noticed like if you want to really dig deeper into it behavior seem like a pretty similar so topicality maybe kind of important but maybe not in the first pass where you're kind of just trying to bootstrap the problem and now detail is actually so in this graph oh here are actually it's a great point so basically these lines are offset where this is like a line 0 because otherwise all of them kind of start to overlap so it will be very hard to see so this is basically time in respect to the user so if you are in New York and it's midnight basically this is midnight for San Francisco, midnight for New York midnight for Tokyo at the time zone time so it's basically like midnight of the user and now actually detail is going to talk a little bit about the personalized schedule hello everyone I'm Aditya like Nemanja said that most of these personalized schedules most of these schedules need to be more on a personalized basis otherwise you're going to get this kind of you can get an aggregate sense of what are the best times to post for a city as a whole but since each individual within the city is unique the personalized schedule has to be unique per individual as well so in some sense you can almost think of it that the audience profile for a given user is kind of like a fingerprint in that sense that each audience profile for a user is unique which means that the schedule for that user is also going to be very unique so I'm just going to quickly walk through some of the simplified model of how we think about this problem so the way we do it is that we consider a very simple social network graph in this case so let's say we have an author A0 and we have some audience member B0B1 up to BM for that author and for each of these audience members you then have he's connected to multiple authors such as A0A1AN and of course there may be an overlap between these guys and these guys but for simplicity's sake we can just consider them to be separate for now so the way we think of this problem is that an author creates a post and an audience member creates a reaction to the post and we are interested in certain questions that we want to answer the first one is when do the authors actually create posts and since we want to calculate the probability we are now looking at like the historical data that we have so we are looking at in the past what is the time when all the authors created posts given that a certain author created a post then when does this audience member react to this to this post and the third question that we have to answer is given that this user is also connected to a lot of other users what is the probability that he reacts to the post created by A0 so this is kind of a very simplified version of like the problem but it allows you to kind of just build a basic model that you can build upon later so just to get some notation out of the way so there are various kind of actions that we consider for a user so first we want to measure what is the aggregated number of posts created by a user in each time bucket so if you are talking about 15 minute time buckets we want to see what are the kinds of what is the profile of how we created the post there at the same time we want to see what are the posts that are visible to a user as an audience member and given that there are certain number of visible posts you know how to see when do you actually react to those visible posts and if you did react to the visible post when was the post created so in some sense every reaction has a delay and that was the inherent delay that we saw in the previous slides where in twitter it is smaller compared to Facebook so this delayed self reaction is computed as convolution of that post to reaction delay function with the original reaction profile and then given all of these we can then kind of estimate that what is the possible reactions that a user may get in a given time bucket by observing all his audience behavior so the final post schedule then we derive using those estimates as a probability of receiving the reaction on post so this will go further basically the first step that we do is we have to take the simplest version where we only consider a0 and b0 and we forget all the other posts that are visible to b0 over here so we just say that let's say this guy was the author and there are a bunch of people who are connected to him what is the probability that those guys would actually react to something that he is posting and then we react to the post so if we just make this just calculate the sum of the delayed reaction profile for each of this user with respect to a0 we get an estimate of what kind of reactions he would get if he posted in a certain time bucket so this is pretty simplistic so obviously it's not going to capture a lot of information but it's a good kind of starting point to understand further into it we now have this audience member b0 who is trying to react to the initial post has a lot of other visible posts that are incoming to him so in some sense all of these guys are trying to grab this person's attention and so we somehow want to model that as well so the way we do that is basically we compute the visible posts that are visible so we do that particular audience member b0 and this could basically just be modeled as a linear function of all the posts created by so if you go back to this slide so if all of these guys are creating posts then the visible posts can be modeled as a linear function of that because each social network would have some sort of an algorithm of how they show the posts to a given user so if that is the case in this case would this be the total reactions that he gets divided by this possible visible post so in some sense this is the attention model that we are talking about so that is again it captures basically the simple version of the graph but in reality what is going to happen is that you are not every audience member interacts with the original user in the same way so you are going to have friends like these always going to comment on everything that you post or always going to like your posts and then you are going to have the other kind of group of your audience who may once in a while throw a like at you but not really care about most of the time what you are trying to post so the modification that we can do to the original kind of estimates is that we can weight each audience member's reactions by his past engagement with the original user so basically that maybe there is your best friend always 90% of the time whenever you post something is going to have reaction to it then you are going to weight his reactions with that kind of of the point 9 whereas if somebody else who has only reacted to your post like maybe once in the last month is not going to get a very high weight when you are trying to estimate the reactions that you are going to get basically this purple function here is the post to reaction delay function so what it says is that so we are looking at a particular user on twitter and it says that within certain time this is almost died to zero so it is only like this kind of small window that you are actually seeing that reactions appear to your posts so if you do the convolution so here in your aggregated reactions that you see from your past so this is the aggregated reactions the green dash line and then you can calculate your first degree weighted schedule that will look something like this and your second degree which has a slightly different shape here and then you can this is of course the subset of maybe a given day and then you can have this same behavior observed over the week and evaluate these kinds of different schedule so the way we did that is we took 56 days of data that was not seen before so we computed these estimates in probabilities and we wanted to evaluate it on the unseen data so we had around half a million active users in that time window and we compared it to two baselines where the most frequently used observed times where people posted the most often was used as one baseline to see how effective it was compared to these schedule and the other baseline was if you just looked at just plain simple first degree schedules and you aggregated that across all users and said that this is a global schedule that everybody follows how does that compare in most of these baselines usually and the metric that we used was to see how much gain in reactions a user would get if he used some of these profiles that we created or if they just versus if they just posted randomly against these baselines so here we just see that most of these baselines are just slightly above on Facebook just slightly above one whereas the best performing schedule is actually the first degree weighted schedule which leads to almost a 17% increase in the reaction gain followed by the second degree schedule which models some of the second degree behavior leads to around 9%. On Twitter it's interesting because Twitter you don't actually see a very large difference as compared to Facebook so on Twitter the maximum gain observed was around 4% and that is mainly again for the first degree weighted schedule and for the second degree weighted schedule actually does better here compared to the plain second degree schedule so this is basically the evaluation tells us that in these models that we considered the first degree weighted schedule actually performed the best for among our users Any questions about that? So that part is not part of this study as such but I guess that is something that could be analyzed if you probably looked at how the number of followers would grow over time and what events occurred where user got a lot of followers so maybe it was a funny cat video that he posted or maybe it was a photograph of him in some celebrity so I guess a lot of it probably depends on content but it's something that we haven't actually studied so I'm just guessing at this point Right So in this service you could actually schedule your post at a given time so you could say that I want to post even though I'm at this point right now I'm just going to post it later So Nimania is going to talk a little more about the open data set for this study So in this case that was not a factor that we considered because the graph that we are looking at is the reaction graph so you may have maybe a thousand followers but out of a thousand followers maybe there are only 50 who are actually reacting to the content that you posted so it is these reactions that we are creating and creating an interaction graph so you're not actually observing your followers as such you're observing the people who are actually reacting to something that you create Yeah, it's normal across users and followers probably just going to cloud.com and figure out what's the right content to share because usually it bubbles up organically So basically one of the main contributions of this work is actually opening the data set so what we did we opened the data set of post reaction time stamps this includes a total of 144 million posts 120 million roughly from Twitter 25 million from Facebook and this includes about 1.1 billion reactions So a pretty large data set hopefully Dimiya can use to kind of replicate and improve some of these studies and the data set basically includes user IDs fingerprint actor ID fingerprinted post ID fingerprint so basically if it was message ID just like a fingerprinted and then post time stamp action time stamp and then user time zone and if anybody is interested using data set can go like basically to cloud to give cloud open data download it try to use it if you think like we can add something more in unanimized fashion we can work with you and kind of expose even more if possible and yeah the time stamps are a little bit perturbed so the user couldn't kind of use the time stamps to decode and de-anonymize the users but yeah give it a go and let us know if we can help somehow so some of the future work regarding like this problem like one thing that we did in this study we kind of generalize the post reaction filter function for a given network but we saw like the different depending on different factors like topics or number of audience in degrees like there may be some differences so kind of getting like more sophisticated personalized post reaction filter function would be one of the areas then getting more sophisticated second degree model would be another thing that we would like to look more deeper into because in the case of the twitter actually second degree was like the second best model but so there is a little bit more room for improvement there but basically just trying to model appropriately how does user get saturated with all the incoming posts and then the another one is topical awareness well like when you do your post does help and we saw like it can help you gain like maybe 20% so it makes content viral is actually like what you post you know if the content is not really good and you post it at the best time to post well you still won't get any reaction so that's one thing that you want to analyze as well and then content analysis also like depending like if you post like just a message text or video maybe there be like different optimal times to post and in addition there are like a more signals more networks and then in conclusion we saw like that different reactions that different post reaction times are different on the different networks we saw like that on twitter the reactions are obtained like 4 times faster than on other networks we saw that audience behavior varies significantly across the networks as for different locations user exhibit different behavior patterns and then we saw that also compared to the baseline using personalized schedules we can get additional gain of maybe 17% on facebook or 4% gain of the reactions on twitter if you post things at the optimal time and in addition we opened the data set and hope like there is going to be some use of it if you like this talk please go to Archivex you can download the full paper which was presented this year's KDD so