Who says what to whom on twitter

By Shaomei Wu, Jake M. Hofman, Winter A. Mason, Duncan J. Watts. 2011

996 citations recorded by the source corpus.

Subfields: Communication, Statistical and Nonlinear Physics

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Communication

In Statistical and Nonlinear Physics

Citations in the corpus

On the Interpretation of Digital Trace Data in Communication and Social Computing Research (2014, neutral): “While two offer no additional interpretation (Achrekar, Gandhe, Lazarus, Yu, & Liu, 2011; Wu, Hofman, Mason, & Watts, 2011), the rest go further.”
Integrating explanation and prediction in computational social science (2021, background): “These efforts have provided insights into how content spreads, some of which align with ideas put forth several decades ago 56 and others that challenge them 57 .”
News Sharing in Social Media: A Review of Current Research on News Sharing Users, Content, and Networks (2015, neutral): “, 2010; Ferrara, Interdonato, & Tagarelli, 2014; Horan, 2013), number of followers or friends (e.g., Bakshy, Hofman, Mason, & Watts, 2011; Hong, Dan, & Davison, 2011; Wu et al., 2011), and news and content preferences (e.”
Online and Social Media Data As an Imperfect Continuous Panel Survey (2016, background): “Out-side of this community, there are limited examples of research utilizing the full Twitter stream [62], but those are mainly focused on examining the network, not the content.”
Homophily, Group Size, and the Diffusion of Political Information in Social Networks: Evidence from Twitter (2016, positive): “Wu et al. (2011) examine over 200 million Tweets during 2009 and 2010 and find that elite users, those contributing a large amount of content on Twitter, are more likely to retweet tweets from each other than tweets by a non-elite user, suggesting heterogeneous effects of communications within a…”
Two 1%s Don't Make a Whole: Comparing Simultaneous Samples from Twitter's Streaming API (2014, methodology): “Geo-spatial filters, network-based sampling algorithms [5] and user-based sampling [6], amongst several other approaches, have all been used to capture data from Twitter.”
Detecting Automation of Twitter Accounts: Are You a Human, Bot, or Cyborg? (2012, background): “Wu et al. [16] studied the information diffusion on Twitter, regarding the production, flow, and consumption of information.”
Towards more systematic Twitter analysis: metrics for tweeting activities (2013, background): “A small but growing number of studies explicitly analyse social media networks (Cha, Haddadi, Benevenuto, & Gum-madi, 2010; Wu, Hofman, Mason, & Watts, 2011).”
Tweets are forever: a large-scale quantitative analysis of deleted tweets (2013, background): “A large volume of research has been conducted on Twitter, including social network topology and properties [13, 11] as well as user classiﬁcations, behaviors, and topics of tweets [27, 26, 11, 10, 12].”
Deep Twitter diving: exploring topical groups in microblogs at scale (2014, methodology): “Most of the prior studies on Twitter have focused exclusively on the popular topics [30, 2, 14] and the presence of a large number of niche topical groups has largely been ignored in the literature.”
Predicting Responses to Microblog Posts (2012, background): “An extensive line of work characterizes users (Pear Analytics, 2009) and quantiﬁes user inﬂuence (Cha et al., 2010; Romero et al., 2011; Wu et al., 2011; Bakshy et al., 2011).”
Social Clicks: What and Who Gets Read on Twitter? (2016, neutral): “Prior works demonstrated that news content exposure benefits from a set of information intermediaries [29, 20], proposed multiple metrics to quantify influence on social media like Twitter [4, 3], proposed models to predict its long term effect [14, 16], and designed algorithms to leverage inherent ”