TwitterStand: news in tweets

By Jagan Sankaranarayanan, Hanan Samet, Benjamin E. Teitler, Michael D. Lieberman, Jon Sperling. ACM SIGSPATIAL International Workshop on Advances in Geographic Information Systems, 2009

771 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/bfe78d4267851cca7f4458f399252ced57209628

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Citations in the corpus

Supervised Text-based Geolocation Using Language Models on an Adaptive Grid (2012, methodology): “1 Geolocation has also been used as a feature in automatic news story identiﬁcation systems (Sankaranarayanan et al., 2009).”
Sampling Content from Online Social Networks (2015, methodology): “Hybrid sampling strategies have also been used; for example, Sankaranarayanan et al. [2009] combined the Gardenhose with tweets from 2,000 hand-picked news media accounts to identify breaking news from Twitter.”
Discovering users' topics of interest on twitter: a first look (2010, background): “We then develop a topic pro.le, which characterizes users topics of interest, by discerning which categories appear frequently and cover the entities.”
Predicting the Political Alignment of Twitter Users (2011, background): “events [9], and the identification of breaking news stories [10]– [12].”
Truthy: mapping the spread of astroturf in microblog streams (2010, methodology): “developed an automated breaking news detection system based on the linking behavior of Twitter users [44], while Heer and Boyd describe a system for visualizing”
Short text classification in twitter to improve information filtering (2010, background): “Sankaranarayanan et al [6] introduce TweetStand to classify tweets as news and non-news.”
Sensing Trending Topics in Twitter (2013, methodology): “TwitterStand [20] uses a “leaderfollower” clustering algorithm that takes into account both textual similarity and temporal proximity.”
Detecting and Tracking Political Abuse in Social Media (2011, methodology): “Sankaranarayanan et al. (2009) developed an automated breaking news detection system based on the linking behavior of Twitter users, while Heer and boyd (2005) describe a system for visualizing and exploring the relationships between users in large-scale social media systems.”
Improving Lexical Semantics for Sentential Semantics: Modeling Selectional Preference and Similar Words in a Latent Variable Model (2013, background): “…(Guo and Diab, 2010; Guo and Diab, 2012a), summarization (Zhou et al., 2006), text coherence (Lapata and Barzilay, 2005), tweet clustering (Sankaranarayanan et al., 2009; Jin et al., 2011), etc. SS operates in a very small context, on average 11 words per sentence in Semeval-2012 dataset…”
Measuring geographical regularities of crowd behaviors for Twitter-based geo-social event detection (2010, background): “[12] also investigated the use of Twitter to construct a news processing system, called as TwitterStand, which automatically obtain breaking news from the tweets posted by Twitter users.”
Hip and trendy: Characterizing emerging trends on Twitter (2011, background): ““Bursts” of interest and attention can be detected in this data in hindsight (Becker, Naaman, & Gravano, 2010; Chen & Roy, 2009; Kleinberg, 2003; Rattenbury, Good, & Naaman, 2007) or in almost real time (Sakaki et al., 2010, Sankaranarayanan, Samet, Teitler, Lieberman, & Sperling, 2009).”
Beyond Trending Topics: Real-World Event Identification on Twitter (2011, methodology): “Several research efforts have focused on identifying events in social media in general, and on Twitter in particular [3, 7, 20, 21].”
Information credibility on twitter (2011, background): “to track epidemics [17], detect news events [28], geolocate such events [27], and find controversial emerging controversial topics [24].”