A few chirps about twitter

By Balachander Krishnamurthy, Phillipa Gill, Martin F. Arlitt. Workshop on Online Social Networks, 2008

802 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/8a583c4e638560fa5963bdb4a79db93575d45762

How researchers see this paper

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

Language Differences and Metadata Features on Twitter (2010, background): “Previous studies have presented detailed characterizations of the Twitter service, and identified distinct classes of users based on their messaging, social and other behaviors [3, 2, 4].”
On the Interpretation of Digital Trace Data in Communication and Social Computing Research (2014, background): “Krishnamurthy et al. (2008) and Java et al. (2007) derive their user categories from quantitative analyses of follower patterns, while boyd et al.’s (2010) observations about Twitter users’ ‘‘imagined audiences’’ emerge from crowdsourcing (see also Golder & Yardi, 2010; Yang & Leskovec, 2010).”
Correlating financial time series with micro-blogging activity (2012, background): “[17, 19, 16]) have analyzed Twitter data to describe the different types of users, their behavior, the content of the tweets and the way that they are related to trends.”
Opinion Mining and Information Fusion: A survey (2016, methodology): “The second corresponds to the use of Web crawlers in order to scrape the data from the desired websites [26] [27] [28].”
Dynamical classes of collective attention in twitter (2011, background): “Several aspects of Twitter have been extensively investigated in the literature, including its network topology [18, 19, 20], the relations and types of messages between users [21, 22], the internal information propagation [23, 24, 25], the credibility of information [26, 27], and even its…”
Language Matters In Twitter: A Large Scale Study (2011, neutral): “Only a few studies explicitly accounted for the international nature of Twitter's proliferation (Java et al. 2007, Krishnamurthy et al. 2008).”
Empirical study of topic modeling in Twitter (2010, background): “Categories and Subject Descriptors H.3.3 [Information Storage and Retrieval]: Information Search and Retrieval General Terms Algorithms, Design, Experimentation Keywords Twitter, Topic Models, Social Media 1.”
Twitter adoption and use in mass convergence and emergency events (2009, background): “Both the DNC and the RNC are large conventions where the two major American political parties (Democratic and Republican respectively) decide who will represent their party in the United States presidential election.”
Walking in Facebook: A Case Study of Unbiased Sampling of OSNs (2010, methodology): “This technique has been used in the past for P2P sampling [8], recently for a few OSNs [9], [10], but not for Facebook.”
Tweets from Justin Bieber's heart: the dynamics of the location field in user profiles (2011, background): “[12] examined the growth of users in each continent over time.”
The Tweets They Are a-Changin: Evolution of Twitter Users and Behavior (2014, result): “In terms of users' self-reported lang, our findings supports the previous findings by Krishnamurthy (Krishnamurthy , Gill, and Arlitt 2008) about the top 10 languages on Twitter in 2008.”
Sampling Content from Online Social Networks (2015, background): “Speciﬁcally for the Twitter OSN, [Krishnamurthy et al. 2008] made the ﬁrst attempt to compare two datasets obtained by different sampling techniques—one through snowball crawling of the Twitter network starting from a small seed set of users, and the other through continuously accessing the Twitter…”
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].”
Microblogging during two natural hazards events: what twitter may contribute to situational awareness (2010, background): “Research on Twitter is still in its infancy, with initial studies [11, 15] concentrating on descriptive properties of Twitter use; statistical accountings of user properties and number of posts broadcast.”
Where in the World Are You? Geolocation and Language Identification in Twitter* (2013, background): “In contrast to the free form nature of the profile location, Krishnamurthy et al. (2008) opt to use timezone (UTC offset) information in a user’s profile to get a user’s local time and thereby approximate longitude.”
Streaming First Story Detection with application to Twitter (2010, background): “On the other hand, research that uses Twitter has so far only focused on describing the properties of Twitter itself (Java et al., 2007; Krishnamurthy et al., 2008).”