Influence and passivity in social media

By Daniel M. Romero, Wojciech Galuba, Sitaram Asur, Bernardo A. Huberman. ECML/PKDD, 2010

761 citations recorded by the source corpus.

The ever-increasing amount of information flowing through Social Media forces the members of these networks to compete for attention and influence by relying on other people to spread their message. A large study of information propagation within Twitter reveals that the majority of users act as passive information consumers and do not forward the content to the network. Therefore, in order for individuals to become influential they must not only obtain attention and thus be popular, but also overcome user passivity. We propose an algorithm that determines the influence and passivity of users based on their information forwarding activity. An evaluation performed with a 2.5 million user dataset shows that our influence measure is a good predictor of URL clicks, outperforming several other measures that do not explicitly take user passivity into account. We demonstrate that high popularity does not necessarily imply high influence and vice-versa.

View Paper: https://www.semanticscholar.org/paper/a0c74b3ed67c5b6665bccec7632d809a04505819

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Citations in the corpus

Social media and political communication: a social media analytics framework (2012, background): “Basically, influence is determined by many factors, such as the novelty and resonance of their messages with those of their audience and the quality and frequency of the content they generate (Romero et al. 2011).”
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).”
Truthy: mapping the spread of astroturf in microblog streams (2010, background): “Other authors have focused on how passive and active users influence the spreading paths [42].”
How social media influencers affect behavioural intentions towards recommended brands: the role of emotional attachment and information value (2021, background): “This utility can be defined in terms of perceived interest, usefulness, novelty or quality of the influencers’ contents (e.g. Djafarova & Rushworth, 2017; Lou & Yuan, 2019; Romero et al., 2011; Xiao et al., 2018), that is, the perceived value of the information they transmit (Lee et al., 2014).”
Intrinsic vs. Image-Related Utility in Social Media: Why Do People Contribute Content to Twitter? (2013, background): “…and the nature of the Twitter social network, and on issues related to influence and information diffusion on this network (see for example Cha et al. 2010; Kwak et al. 2010; Weng, Lim and Jiang 2010; Bakshy et al. 2011; Romero et al. 2011; Wu et al. 2011; Goel, Watts, and Goldstein 2012).”
Procedia Social and Behavioral Sciences (unknown): “Also, Romero et al. (2010) showed that the correlation between popularity and influence is weaker than it might be expected, because most users are passive information consumers and do not forward the content to the network.”
The role of digital influencers in brand recommendation: Examining their impact on engagement, expected value and purchase intention (2019, unknown): “…actual influence of influencers taking into account evidence questioning whether the number of followers and other direct indicators of popularity (e.g., profile views) do in fact automatically imply high influence (De Veirman et al ., 2017; Romero et al ., 2011; Trusov, Bodapati, & Bucklin, 2010).”
Information diffusion in online social networks: a survey (2013, methodology): “[38] develop a graph-based approach similar to the well known HITS algorithm, IP (i.”
Microblogging after a major disaster in China: a case study of the 2010 Yushu earthquake (2011, background): “Factors related to tweet content, user similarity, and user personality were explored [3,4,13, 18 ,22,27].”
Bad news travel fast: a content-based analysis of interestingness on Twitter (2011, methodology): “The question of what causes a message to be retweeted has frequently been addressed, but mainly in a scenario of retweet prediction for a given user and with a focus on the structure of the social network [4, 9, 13].”
Demographics of News Sharing in the U.S. Twittersphere (2017, background): “Not surprisingly, a number of efforts have attempted to quantify and characterize information spread in social networks as well as the role users play in such propagation [11, 12, 36, 37, 42].”
Twitter's Glass Ceiling: The Effect of Perceived Gender on Online Visibility (2016, background): “User behaviors affect attention: for instance, users who post more or post about certain topics may attract more attention online (Romero et al. 2011; Wu et al. 2011; Weng et al. 2010).”
Trends in Social Media: Persistence and Decay (2011, background): “Recently, Romero and others [9] introduced a novel in-ﬂuence measure that takes into account the passivity of the audience in the social network.”