Why we twitter: understanding microblogging usage and communities

By Akshay Java, Xiaodan Song, Timothy W. Finin, Belle L. Tseng. 2007

3,211 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Statistical and Nonlinear Physics

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

In Computer Networks and Communications

In Statistical and Nonlinear Physics

Citations in the corpus

Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019, background): “Such analyses capture the distribution, variability, or correlations among variables, such as Java et al. (2007)—one of the ﬁrst studies to characterize the growth, topological, and geographical properties of Twitter using descriptive statistics.”
Averaging Gone Wrong: Using Time-Aware Analyses to Better Understand Behavior (2016, neutral): “People in a cohort are assumed to be exposed to the same state of the world and thus are more comparable to each other than to people in other cohorts.”
#TwitterSearch: a comparison of microblog search and web search (2011, background): “[14] identified “information seekers” as a primary category of Twitter users.”
Online and Social Media Data As an Imperfect Continuous Panel Survey (2016, neutral): “[54] develops a similar taxonomy for Twitter.”
Empirical study of topic modeling in Twitter (2010, background): “We also discuss how the state-ofthe-art Author-Topic model fails to model hierarchical relationships between entities in Social Media.”
Deep Twitter diving: exploring topical groups in microblogs at scale (2014, background): “…broad categories: (i) graph-based approaches : those that rely primarily on applying community detection algorithms on the Twitter follow-network [14, 28], and (ii) semantic approaches : those that rely on identifying groups of users with similar interests by analyzing either their proﬁle…”
Studying political microblogging: Twitter users in the 2010 Swedish election campaign (2012, background): “The two other anonymous users are Skogskant and Nemokrati, the latter of which claims to be an author using Twitter under an assumed name.”
How Twitter is Changing the Nature of Financial News Discovery (2016, background): “easily digestible form of its content, because most tweets are shared publicly, and because it has historically had a news focus [30, 25].”
Using twitter to recommend real-time topical news (2009, background): “Recently there has been much interest in Twitter, partly because of its growth [2], and partly because of its ability to provide access to thoughts, intentions and activities of millions of users in real-time.”
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-spread-ing process, and to discover emerging topics of interesest over such a network [6, 7, 15].”
Sentiment Knowledge Discovery in Twitter Streaming Data (2010, methodology): “[15] found communities using HyperText Induced Topic Search (HITS) [17], and the Clique Percolation Method [8].”
Information, Community, and Action: How Nonprofit Organizations Use Social Media (2012, background): “As for Twitter, several attempts at tweet categorization have been made (Java et al., 2007; Naaman et al., 2010), but none have looked at organizational applications.”
Twitter's Glass Ceiling: The Effect of Perceived Gender on Online Visibility (2016, background): “The gender differences in topical expertise and interests on Twitter may explain the differences in visibility (Java et al. 2007).”
Truthy: mapping the spread of astroturf in microblog streams (2010, background): “Social media, such as Twitter, often enjoy substantial user bases with participants drawn from diverse geographic, social and political backgrounds [29, 3] Moreover, the user-as-information-producer model provides researchers and news organizations alike a means of instrumenting and observing, in re”
Political Communication and Influence through Microblogging--An Empirical Analysis of Sentiment in Twitter Messages and Retweet Behavior (2012, background): “[21] examined the topological and geographical properties of Twitter’s social network.”
Testing Propositions Derived from Twitter Studies: Generalization and Replication in Computational Social Science (2015, background): “Even though some studies confirmed that power-law exponents are between 2 and 3, others found that the follower or followee distribution is not fit by power law, or their power-law exponents are less than 2 if the distributions are power law [5, 6, 16, 25].”