| Alessandro Flammini|
(Alternative names for this author)
|Co-authors||Alessandro Vespignani, Filippo Menczer, Jacob Ratkiewicz, Santo Fortunato|
|Authorship||Publications (3), datasets (0), tools (0)|
|Citations||Total (5), average (1.66666666667), median (1), max (3), min (1)|
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Alessandro Flammini is an author.
PublicationsOnly those publications related to wikis are shown here.
|Title||Keyword(s)||Published in||Language||DateThis property is a special property in this wiki.||Abstract||R||C|
|Characterizing and modeling the dynamics of online popularity||Physical Review Letters||2010||Online popularity has an enormous impact on opinions, culture, policy, and profits. We provide a quantitative, large scale, temporal analysis of the dynamics of online content popularity in two massive model systems: the Wikipedia and an entire country's Web space. We find that the dynamics of popularity are characterized by bursts, displaying characteristic features of critical systems such as fat-tailed distributions of magnitude and interevent time. We propose a minimal model combining the classic preferential popularity increase mechanism with the occurrence of random popularity shifts due to exogenous factors. The model recovers the critical features observed in the empirical analysis of the systems analyzed here, highlighting the key factors needed in the description of popularity dynamics. 2010 The American Physical Society.||0||3|
|Traffic in social media I: Paths through information networks||Proceedings - SocialCom 2010: 2nd IEEE International Conference on Social Computing, PASSAT 2010: 2nd IEEE International Conference on Privacy, Security, Risk and Trust||English||2010||Wikipedia is used every day by people all around the world, to satisfy a variety of information needs. We cross-correlate multiple Wikipedia traffic data sets to infer various behavioral features of its users: their usage patterns (e.g., as a reference or a source); their motivations (e.g., routine tasks such as student homework vs. information needs dictated by news events); their search strategies (how and to what extent accessing an article leads to further related readings inside or outside Wikipedia); and what determines their choice of Wikipedia as an information resource. We primarily study article hit counts to determine how the popularity of articles (and article categories) changes over time, and in response to news events in the English-speaking world. We further leverage logs of actual navigational patterns from a very large sample of Indiana University users over a period of one year, allowing us unprecedented ability to study how users traverse an online encyclopedia. This data allows us to make quantitative claims about how users choose links when navigating Wikipedia. From this same source of data we are further able to extract analogous navigation networks representing other large sites, including Facebook, to compare and contrast the use of these sites with Wikipedia. Finally we present a possible application of traffic analysis to page categorization.||0||1|
|Traffic in social media II: Modeling bursty popularity||Proceedings - SocialCom 2010: 2nd IEEE International Conference on Social Computing, PASSAT 2010: 2nd IEEE International Conference on Privacy, Security, Risk and Trust||English||2010||Online popularity has enormous impact on opinions, culture, policy, and profits, especially with the advent of the social Web and Web advertising. Yet the processes that drive popularity in our online world have only begun to be explored. We provide a quantitative, large scale, longitudinal analysis of the dynamics of online content popularity in two massive model systems, the Wikipedia and an entire country's Web space. In these systems, we track the change in the number of links to pages, and the number of times these pages are visited. We find that these changes occur in bursts, whose magnitude and time separation are very broadly distributed. This finding is in contrast with previous reports about news-driven content, and has profound implications for understanding collective attention phenomena in general, and Web trends in particular. To make sense of these empirical results, we offer a simple model that mimics the exogenous shifts of user attention and the ensuing non-linear perturbations in popularity rankings. While established models based on preferential attachment are insufficient to explain the observed dynamics, our stylized model is successful in recovering the key features observed in the empirical analysis of our systems.||0||1|