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BEGIN:VEVENT
DTSTAMP:20260614T195936Z
DTSTART;VALUE=DATE-TIME:20151118T130000
DTEND;VALUE=DATE-TIME:20151118T140000
SUMMARY:Complexity Forum: Mariano Beguerisse-Diaz (Imperial College Londo
 n)
TZID:Europe/London
UID:20151118-094d43454c4729f8014c55c989074e20@warwick.ac.uk
CREATED:20150326T110652Z
DESCRIPTION:Using structure and content to reveal the evolution of narrat
 ives in social media The wealth of data available from a variety of sour
 ces presents attractive opportunities in academia and beyond. Analysing 
 large datasets and extracting useful information from them is not a triv
 ial task. Often\, collections of data have several layers of structure\,
  are complex and noisy. Data from social media and other sources can be 
 processed in many ways\; recently we have studied a dataset of relations
 hips among Twitter users who were prominent during the 2011 riots in Eng
 land. These data consist of the names and descriptions of the users and 
 their mutual relationships (i.e.\, who follows whom). Although the data 
 did not include the actual messages that passed through these links duri
 ng the riots\, we are able to study the structure of the relationships t
 o reveal information about the users\, their interests\, hierarchies and
  roles. Analyses of the network structures created by relationships or i
 nteractions between the data-generating agents\, however\, cannot answer
  questions such as: what topics do users of social media talk about\, an
 d how do these topics and their user participation change in time? To fi
 nd answers we must go beyond the meta-data and look at the content produ
 ced by the users. We have developed a method to study large\, longitudin
 al collections of textual data that allows us to understand the evolutio
 n of discourse and group narratives. Our method uses topic timelines\, a
  concept we have recently introduced. Topic timelines are networks whose
  nodes are content-units (such as topics) that appear in a given time in
 terval\; the edges may depend on the shared authors between the nodes\, 
 topical similarity\, etc. Handling data in this way creates tractable ne
 tworks that\, for example\, not only reveal what topics appear when\, bu
 t also help to understand the relationship among the different topics in
  terms of agent participation or similarity. These new networks can be e
 xplored using standard tools from network science. For example\, by extr
 acting their communities we can track the origin\, evolution\, and decli
 ne of collective narratives\; we can identify which seemingly disparate 
 topics are related through their common users\, obtain the user turnover
  of topics\, or know when a topic becomes exhausted for some groups of u
 sers but not for others. This method provides a way to make large and co
 mplex sets of longitudinal textual data tractable and amenable to analys
 is with the rich palette of tools from network science\, offering a new 
 point of view from which collective discourse can be studied. We showcas
 e our method on collections of Twitter status updates which include conv
 ersations about obesity diabetes and the UK's National Health Service. T
 his methodology is applicable not only to social media but to any collec
 tion of longitudinal data generated by large numbers of agents.
LOCATION:D1.07
CATEGORIES:forum
LAST-MODIFIED:20151117T114758Z
ORGANIZER;CN=Heather Robson:
END:VEVENT
END:VCALENDAR