Quantifying Collective Attention from Tweet Stream

Abstract

Online social media are increasingly facilitating our social interactions, thereby making available a massive “digital fossil” of human behavior. Discovering and quantifying distinct patterns using these data is important for studying social behavior, although the rapid time-variant nature and large volumes of these data make this task difficult and challenging. In this study, we focused on the emergence of “collective attention” on Twitter, a popular social networking service. We propose a simple method for detecting and measuring the collective attention evoked by various types of events. This method exploits the fact that tweeting activity exhibits a burst-like increase and an irregular oscillation when a particular real-world event occurs; otherwise, it follows regular circadian rhythms. The difference between regular and irregular states in the tweet stream was measured using the Jensen-Shannon divergence, which corresponds to the intensity of collective attention. We then associated irregular incidents with their corresponding events that attracted the attention and elicited responses from large numbers of people, based on the popularity and the enhancement of key terms in posted messages or “tweets.” Next, we demonstrate the effectiveness of this method using a large dataset that contained approximately 490 million Japanese tweets by over 400,000 users, in which we identified 60 cases of collective attentions, including one related to the Tohoku-oki earthquake. “Retweet” networks were also investigated to understand collective attention in terms of social interactions. This simple method provides a retrospective summary of collective attention, thereby contributing to the fundamental understanding of social behavior in the digital era.

Reply trees in Twitter: data analysis and branching process models

“…A classical example is diffusion of technological innovation, in which individuals receiving information on a new technology from other peers may decide to adopt the technology (Rogers 2003;Easley and Kleinberg 2010). Other examples include fads (Gladwell 2000), social mobilization (Lotan et al 2011;Banõs et al 2013;Conover et al 2013), marketing (Leskovec et al 2007a;Easley and Kleinberg 2010), voter turnout (Bond et al 2012), responses to natural disasters (Sano et al 2013;Sasahara et al 2013), and circulation of new scientific publications (Thelwall et al 2013) to name but a few.…”

“…We use Twitter data because Twitter is suitable for studying diffusion processes for several reasons (Kwak et al 2010;Bakshy et al 2011;Bollen et al 2011;Dodds et al 2011;Lotan et al 2011;Bliss et al 2012;Cogan et al 2012;Banõs et al 2013;Conover et al 2013;Sasahara et al 2013). First, Twitter is devoted to information diffusion.…”

Public Opinion Formation: The Role Of Independent Media Portal During Crisis

“…Jafari Asbagh et al (2014) propose a streaming framework for detecting and clustering memes in online social networks. Meanwhile, tracking popular topics or emergent events is also an effective way to study the dynamics of collective attention or collective response (Bagrow et al, 2011), which essentially drives the formation of trends or spikes (Asur et al, 2011;Bao et al, 2013b;Wu & Huberman, 2007;Gomez Rodriguez et al, 2010;Lin et al, 2011;Romero et al, 2011b;Bauckhage et al, 2014;Sasahara et al, 2013;Ferrara et al, 2013;Bagrow et al, 2011;Ferrara et al, 2014). Stroud et al (2014) indicate that a news organization can affect the deliberative behaviour of commenters by tracking and cultivating deliberative norms via news organization involvement.…”

The Spontaneous Behavior in Extreme Events: A Clustering-Based Quantitative Analysis

“…Therefore, it is difficult for us to predict human behaviors utilizing previous empirical studies. With the popular of social media, the spontaneous emergence of online human behaviors can be tracked by Micro-blogging systems such as Twitter [5,6], which contains some hashtags standing for events or objects in its tweets [6][7][8]. So, it is practicable for us to perceive people's feelings or reactions towards different social or natural events that may emerge suddenly in the real world based on these textual information [9].…”

“…In their studies, an event is extracted by some keywords (e.g., hashtags in Twitter) [6][7][8]. However, an event is often accompanied by a series of episodes [12], which are associated subevents within the entire event [13].…”