Everyone's an influencer: quantifying influence on twitter

By Eytan Bakshy, Jake M. Hofman, Winter A. Mason, Duncan J. Watts. 2011

1,971 citations recorded by the source corpus.

Subfields: Sociology and Political Science, Statistical and Nonlinear Physics

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Citations in the corpus

How algorithmic popularity bias hinders or promotes quality (2017, background): “Furthermore, knowledge of what is popular can be construed as a form of social influence; an individual’s behavior may be guided by choices of peers or neighbors [23, 19, 20, 18, 29, 2, 6].”
How Visibility and Divided Attention Constrain Social Contagion (2012, background): “Recent research suggests that social contagion is a complex process that depends on the type of information being transmitted [29], its novelty [27], influence of the initial spreader [38] and activity levels of subsequent spreaders [39], how people respond to repeated exposure to information by the”
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).”
Competition among memes in a world with limited attention (2012, background): “, the intrinsic quality of the information they sprea”
SIMPATH: An Efficient Algorithm for Influence Maximization under the Linear Threshold Model (2011, background): “selecting informative blogs [3] and finding influential tweeters [4], [5].”
Spread of Hate Speech in Online Social Media (2018, methodology): “All subsequent evaluation is carried out on the DAG generated by the LRIF model.”
The Structural Virality of Online Diffusion (2015, methodology): “Only recently have data satisfying these requirements become available, as a result of online behavior such as blogging (Adar and Adamic 2005), e-commerce (Leskovec et al. 2006), multiplayer gaming (Bakshy et al. 2009), and social networking (Sun et al. 2009, Bakshy et al. 2011, Goel et al. 2012).”
Crowdsourced Data Management: Industry and Academic Perspectives (2015, methodology): “Crowdsourcing is used in spam identification [137], text classification [30, 172], translation [199], and text editing [36].”
Communication Channels and Word of Mouth: How the Medium Shapes the Message (2013, background): “Research using online word-of-mouth data (Bakshy et al. 2011; Berger and Milkman 2012) found that interesting things get talked about more, but research using face-toface word-of-mouth data (Berger and Schwartz 2011) failed to find such effects.”
Information diffusion and external influence in networks (2012, methodology): “Incorporating our model into methods for identifying “influencers” in networks [18, 5, 9] might be fruitful.”
From chatter to headlines: harnessing the real-time web for personalized news recommendation (2012, background): “A number of studies attempts to understand the twitter social network, the information-spreading process, and to discover emerging topics of interesest over such a network [6, 7, 15].”
False Information on Web and Social Media: A Survey (2018, background): “These accounts are synthetically created or compromised [85], controlled by a single underlying entity, and engineer the spread of false information through social networks [11, 19], with the aim of creating an illusion of public consensus towards the false pieces of information.”
On the Interpretation of Digital Trace Data in Communication and Social Computing Research (2014, background): “Somewhat uncharacteristically, Bakshy et al. (2011) discuss in depth the relationship between digital traces of redistribution and the expansive concept of influence.”
What Makes Online Content Viral? (2012, critical): “recent research has cast doubt on its value (Bakshy et al. 2011; Watts 2007) and suggests that it is far from cost effective.”
Online Human-Bot Interactions: Detection, Estimation, and Characterization (2017, methodology): “Here we demonstrate that accounts controlled by software exhibit behaviors that reflects their intents and modus operandi (Bakshy et al. 2011; Das et al. 2016), and that such behaviors can be detected by supervised machine learning techniques.”
Distinguishing between Personal Preferences and Social Influence in Online Activity Feeds (2016, background): “A baseline measure of copy-influence would be the fraction of actions by a user that are copy-actions, as reported in some studies [25, 48, 7].”
Cultural Dimensions in Twitter: Time, Individualism and Power (2013, background): “The number of a user’s followers (indegree) does not necessarily reflect influence but does reflect popularity (Bakshy et al. 2011; Cha et al. 2010; Kwak et al. 2010)).”