Analyzing and predicting viral tweets
Published 13 May 2013
Maximilian Jenders, Gjergji Kasneci, Felix Naumann
Citations198
Generate an AI Snapshot to get a quick, structured summary of this paper.
Study Snapshot
ObjectiveStudy objective
MethodsResearch methodology
PopulationPopulation studied
Sample sizeSample sizes
OutcomesStudy outcomes here
ResultsStudy results comes here
LimitationsResearch study limitations comes here
A concise AI-generated summary of the paper will appear here once you click Generate AI Snapshot.
TL;DR
An extensive analysis of a wide range of tweet and user features regarding their influence on the spread of tweets is provided and the most impactful features are chosen to build a learning model that predicts viral tweets with high accuracy.
Abstract
Twitter and other microblogging services have become indispensable sources of information in today's web. Understanding the main factors that make certain pieces of information spread quickly in these platforms can be decisive for the analysis of opinion formation and many other opinion mining tasks.
Keywords
Computer SciencePhysics and Astronomy
Maximizing the spread of influence through a social network
7,314 Citations2003David Kempe, Jon Kleinberg +1 more
An analysis framework based on submodular functions shows that a natural greedy strategy obtains a solution that is provably within 63% of optimal for several classes of models, and suggests a general approach for reasoning about the performance guarantees of algorithms for these types of influence problems in social networks.
What is Twitter, a social network or a news media?
6,692 Citations2010Haewoon Kwak, Changhyun Lee +2 more
This work is the first quantitative study on the entire Twittersphere and information diffusion on it and finds a non-power-law follower distribution, a short effective diameter, and low reciprocity, which all mark a deviation from known characteristics of human social networks.
Proceedings of the International AAAI Conference on Web and Social MediaMeasuring User Influence in Twitter: The Million Follower Fallacy
3,009 Citations2010Meeyoung Cha, Hamed Haddadi +2 more
An in-depth comparison of three measures of influence, using a large amount of data collected from Twitter, is presented, suggesting that topological measures such as indegree alone reveals very little about the influence of a user.
An empirical study of the naive Bayes classifier
2,549 Citations2001Irina Rish
This work analyzes the impact of the distribution entropy on the classification error, showing that low-entropy feature distributions yield good performance of naive Bayes and demonstrates that naive Baye works well for certain nearlyfunctional feature dependencies.
Predicting the Future with Social Media
2,064 Citations2010Sitaram Asur, Bernardo A. Huberman
Tweet, Tweet, Retweet: Conversational Aspects of Retweeting on Twitter
2,035 Citations2010danah boyd, Su Golder +1 more
This paper examines the practice of retweeting as a way by which participants can be "in a conversation" and highlights how authorship, attribution, and communicative fidelity are negotiated in diverse ways.
Everyone's an influencer
1,659 Citations2011Eytan Bakshy, Jake M. Hofman +2 more
It is concluded that word-of-mouth diffusion can only be harnessed reliably by targeting large numbers of potential influencers, thereby capturing average effects and that predictions of which particular user or URL will generate large cascades are relatively unreliable.
Want to be Retweeted? Large Scale Analytics on Factors Impacting Retweet in Twitter Network
1,179 Citations2010Bongwon Suh, Lichan Hong +2 more
It is found that, amongst content features, URLs and hashtags have strong relationships with retweetability and the number of followers and followees as well as the age of the account seem to affect retweetability, while, interestingly, thenumber of past tweets does not predict retweetability of a user's tweet.
Journal of the American Society for Information Science and TechnologySentiment strength detection for the social web
1,063 Citations2011Mike Thelwall, Kevan Buckley +1 more
An improved version of the algorithm SentiStrength for sentiment strength detection across the social web that primarily uses direct indications of sentiment is assessed, suggesting that, even unsupervised, Senti strength is robust enough to be applied to a wide variety of different social web contexts.
Who says what to whom on twitter
952 Citations2011Shaomei Wu, Jake M. Hofman +2 more
A striking concentration of attention is found on Twitter, in that roughly 50% of URLs consumed are generated by just 20K elite users, where the media produces the most information, but celebrities are the most followed.
Proceedings of the International AAAI Conference on Web and Social MediaModeling Public Mood and Emotion: Twitter Sentiment and Socio-Economic Phenomena
950 Citations2021Johan Bollen, Huina Mao +1 more
It is speculated that large scale analyses of mood can provide a solid platform to model collective emotive trends in terms of their predictive value with regards to existing social as well as economic indicators.
Journal of the American Society for Information Science and TechnologySentiment in Twitter events
810 Citations2010Mike Thelwall, Kevan Buckley +1 more
A study of a month of English Twitter posts is reported, assessing whether popular events are typically associated with increases in sentiment strength, as seems intuitively likely and using the top 30 events as a measure of relative increase in (general) term usage.
Predicting popular messages in Twitter
576 Citations2011Liangjie Hong, Ovidiu Dan +1 more
It is shown that the method can successfully predict messages which will attract thousands of retweets with good performance and formulate the task into a classification problem and study two of its variants by investigating a wide spectrum of features based on the content of the messages.
Journal of Verbal Learning and Verbal BehaviorThe pollyanna hypothesis
538 Citations1969Jerry D. Boucher, Charles E. Osgood
SSRN Electronic JournalSocial Networks that Matter: Twitter Under the Microscope
520 Citations2008Bernardo A. Huberman, Daniel M. Romero +1 more
A study of social interactions within Twitter reveals that the driver of usage is a sparse and hidden network of connections underlying the “declared” set of friends and followers.
Proceedings of the International AAAI Conference on Web and Social MediaRT to Win! Predicting Message Propagation in Twitter
371 Citations2021Saša Petrović, Miles Osborne +1 more
A machine learning approach based on the passive-aggressive algorithm is able to automatically predict retweets as well as humans, and it is found that performance is dominated by social features, but that tweet features add a substantial boost.
Bad news travel fast
354 Citations2011Nasir Naveed, Thomas Gottron +2 more
This paper analyzes a set of high- and low-level content-based features on several large collections of Twitter messages to obtain insights into what makes a message on Twitter worth retweeting and, thus, interesting.
arXiv (Cornell University)Modeling public mood and emotion: Twitter sentiment and socio-economic phenomena
318 Citations2009Johan Bollen, Alberto Pepe +1 more
Predicting Information Spreading in Twitter
179 Citations2010Tauhid Zaman, Ralf Herbrich +2 more
A new methodology for predicting the spread of information in a social network based on data of who and what was retweeted and a probabilistic collaborative filter model to predict future retweets is presented.
User oriented tweet ranking
175 Citations2011İbrahim UYSAL, W. Bruce Croft
This paper proposes a personalized tweet ranking method, leveraging the use of retweet behavior, to bring more important tweets forward, and investigates how to determine the audience of tweets more effectively, by ranking the users based on their likelihood of retweeting the tweets.
EPJ Data SciencePositive words carry less information than negative words
121 Citations2012David García, Antonios Garas +1 more
Taking into account the frequency of word usage, it is found that words with a positive emotional content are more frequently used, which lends support to Pollyanna hypothesis that there should be a positive bias in human expression.
Who gives a tweet?
108 Citations2012Paúl André, Michael S. Bernstein +1 more
A website that collected the first large corpus of follower ratings on Twitter updates finds that users value information sharing and random thoughts above me-oriented or presence updates, and offers insight into evolving social norms.
Repository for Publications and Research Data (ETH Zurich)Emotional persistence in online chatting communities
106 Citations2012Antonios Garas, David García +2 more
Internet MathematicsUsing PageRank to Characterize Web Structure
83 Citations2006Gopal Pandurangan, Prabhakar Raghavan +1 more
It is suggested that PageRank values on the web follow a power law, and generative models for the web graph are developed that explain this observation and moreover remain faithful to previously studied degree distributions.
Effects of the recession on public mood in the UK
81 Citations2012Thomas Lansdall-Welfare, Vasileios Lampos +1 more
A collection of 484 million tweets generated by more than 9.8 million users from the United Kingdom over the past 31 months, a period marked by economic downturn and some social tensions, shows that periodic events such as Christmas and Halloween evoke similar mood patterns every year.
Proceedings of the International AAAI Conference on Web and Social MediaEmotional Divergence Influences Information Spreading in Twitter
80 Citations2021René Pfitzner, Antonios Garas +1 more
It is found that although the overall sentiment (polarity) does not influence the probability of a tweet to be retweeted, a new measure called "emotional divergence" does have an impact, and in general, tweets with high emotional diversity have a better chance of being retweeting, hence influencing the distribution of information.
Lecture notes in computer sciencePredicting Discussions on the Social Semantic Web
50 Citations2011Matthew Rowe, Sofia Angeletou +1 more
An approach for predicting discussions on the Social Web is presented, by identifying seed posts, then making predictions on the level of discussion that such posts will generate, and the use of post-content and user features and their subsequent effects on predictions are explored.
Fine-grained German Sentiment Analysis on Social Media
40 Citations2012Saeedeh Momtazi
A fine-grained annotation for German texts is provided, which represents the sentiment strength of the input text using two scores: positive and negative, and a German opinion dictionary of 1,864 words is prepared and compared with other opinion dictionaries for German.