Details

Autor(en) / Beteiligte

• Grimaudo, Luigi
• Song, Han
• Baldi, Mario
• Mellia, Marco
• Munafò, Maurizio

Titel

TUCAN: Twitter user centric ANalyzer

Ist Teil von

• 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013), 2013, p.1455-1457

Ort / Verlag

New York, NY, USA: ACM

Erscheinungsjahr

2013

Quelle

ACM Digital Library (Association for Computing Machinery)

Beschreibungen/Notizen

• Twitter has attracted millions of users that generate a humongous flow of information at constant pace. The research community has thus started proposing tools to extract meaningful information from tweets. In this paper, we take a different angle from the mainstream of previous works: we explicitly target the analysis of the timeline of tweets from "single users". We define a framework - named TUCAN - to compare information offered by the target users over time, and to pinpoint recurrent topics or topics of interest. First, tweets belonging to the same time window are aggregated into "bird songs". Several filtering procedures can be selected to remove stop-words and reduce noise. Then, each pair of bird songs is compared using a similarity score to automatically highlight the most common terms, thus highlighting recurrent or persistent topics. TUCAN can be naturally applied to compare bird song pairs generated from timelines of different users. By showing actual results for both public profiles and anonymous users, we show how TUCAN is useful to highlight meaningful information from a target user's Twitter timeline.