An empirical approach towards a whom-to-mention Twitter app
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1 An empirical approach towards a whom-to-mention Twitter app Soumajit Pramanik 1, Maximilien Danisch 2, Qinna Wang 2 Jean-Loup Guillaume 3, Bivas Mitra 1 1- CNeRG, IIT Kharagpur, India 2- Complex Networks team, LIP6, UPMC-CNRS, Paris, France 3- L3I, University of La Rochelle Supported by: The French National Research Agency - contract CODDDE ANR-13-CORD and ECIS - Cefipra Indo-French Targeted Program May 12, 2015
2 This is joint work with: Soumajit Pramanik (IIT), Maximilien Danisch (LIP6), Dr. Qinna Wang (LIP6) Prof. Jean-Loup Guillaume (ULR) Prof. Bivas Mitra (IIT) 2/28
3 What is Twitter? Twitter is a social microbloging system which has become one of the most important ways for sharing information online. What kind of information? very local: I ll have a coffee at our bakery in 10min local: IITkgp party on Friday night, all invited global: Nicolas Sarkozy is back to politics very global: There has been a terrorist attack in Paris 3/28
4 Retweets = propagation Where do retweets come from? Someone has to see your tweet: One of your followers One of the followers of someone that retweeted your tweet Someone you Someone who saw your tweet searching for keywords or # Someone who saw it somewhere else on the Internet 4/28
5 Follow network 5/28
6 You can do something 6/28
7 Our goal How to share an information/opinion with the greatest number of interested people? use Twitter + use mention = use our App We target: 1 (very) global tweets and 2 (very) local tweets 7/28
8 Outline 1 Related work on mention recommendation 2 Data study to get insights 3 Online application 4 Experiments faky app 5 Conclusion & perspectives 8/28
9 Three related papers 1- Whom to mention: expand the diffusion of tweets recommendation on micro-blogging systems. Wang et al. WWW2013 PROS First in-depth study of mention in microblogs Extract features (interest match, user relationship and user influence) A ranking function is trained Address recommendation overload problem CONS No real-time experiments No usable app provided 9/28
10 Three related papers 2- Locating targets through mention in Twitter. Tang et al. World Wide Web 2014 PROS Extract four categories of features (content, social, location and time) CONS The goal is to mention someone interested and trigger interactions (mentions, replies, retweets, friendship) but not to maximize the spreading No real-time experiments No usable app provided 10/28
11 Three related papers 3- Who will retweet this? Automatically identifying and engaging strangers on Twitter to spread information. Lee et al. IUI2014 PROS More features extracted for machine learning methods and relevance is evaluated Waiting-time model Real-time experiments CONS Goal is to mention someone so that he retweets, not to maximize the spreading Not real users ( Public Safety News and Bird Flu News ) No usable app provided 11/28
12 What we suggest Make a whom-to-mention recommendation system to maximize the spread of a tweet, working only on real time experiments, with a usable app, and improve the app every time it is used. 12/28
13 Outline 1 Related work on mention recommendation 2 Data study to get insights 3 Online application 4 Experiments faky app 5 Conclusion & perspectives 13/28
14 World cup dataset The dataset consists of all tweets made during May, June or July 2014 and containing hashtags specific to the 2014 soccer world cup. 27,916,100 tweets made by 6,020,228 unique users. 15,249,762 retweets (55%) 937,201 answers (3%) 11,729,137 original tweets (42%) 14/28
15 Data study 1.6 Average number of retweets / Number of mentions
16 Data study 10-1 Retweet probability Number of followers of the mentioned user 16/28
17 Data study Retweet probability times # followers Number of followers of the mentioned user 17/28
18 Outline 1 Related work on mention recommendation 2 Data study to get insights 3 Online application 4 Experiments faky app 5 Conclusion & perspectives 18/28
19 Online application Users database? (very) global tweets use a database of random users (very) local tweets target non reciprocal followees 19/28
20 Online application Target users that are popular: f P (u) are susceptible to retweet the tweet: f RT (u, t) i.e. have a high S(u, t) = f P (u).f RT (u, t) Approximation Neglect overlap (as we mention several users) and maximize the number of people that will see the tweet at hop one. u S(u, t) f P (u) = number of followers of u Learn f RT (u, t) using logistic regression 20/28
21 Logistic regression Principle: Regression (or binary classification of vectors) by fitting 1 parameters of a logistic function: V Θ (X ) = 1+exp(Θ T X ) to optimize a convex quality function. Advantages: Returns the probability for a user to retweet; A few parameters to store: convenient for an online app. 21/28
22 Map into the knapsack problem The goal is to use the remaining place as efficiently as possible: Solve the knapsack problem with: $4 12 kg? 15 kg $2 2 kg total budget: 140 L tweet user weight: 2 + L screenname user value: S(u, t) $2 1 kg $1 1 kg $10 4 kg 22/28
23 Improving the app Every time the app is used we 1 collect users to increase the database 2 check which mentioned user retweets and improve f RT (u, t) 23/28
24 Live Demo 24/28
25 Outline 1 Related work on mention recommendation 2 Data study to get insights 3 Online application 4 Experiments faky app 5 Conclusion & perspectives 25/28
26 Current work: faky app Select tweets containing mention(s) made recently on Twitter by random users and retweet them from our fake account replacing the mention(s). Try to have more retweets! We chose the tweets based on keywords and a manual selection in order to have only global tweets. 50 tweets lead to: 4 retweets, 2 followers, 5 favourites, 6 mentions, 1 mentioned user blocked us. 26/28
27 Outline 1 Related work on mention recommendation 2 Data study to get insights 3 Online application 4 Experiments faky app 5 Conclusion & perspectives 27/28
28 Discussion Impact of number of mentions Is knapsack worth it? Tweeting several times the same tweets mentioning different users Extreme case Do not use friendship... use mention! Twitter2.0 Should not target bad users Sybils (users creating many fake accounts) Social capitalists (FMFY/IFYFM) 28/28
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