Measuring and managing information diets of social media users: Research overview

Juhi Kulshrestha*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

3 Citations (Scopus)

Abstract

The widespread adoption of social media like Twitter and Facebook has lead to a paradigm shift in the way our society is producing and consuming information, from the broadcast mass media to online social media. To study the effects of this paradigm shift, we define the concept of information diet-which is the composition of a set of information items being produced or consumed. Information diets can be constructed along many aspects like topics (eg. politics, sports, science etc), or perspectives (eg. politically left leaning or right leaning), or sources (eg. originating from different parts of the world). We use information diets to measure the diversity and bias in the information produced or consumed, and to study how the recommendation and search systems are shaping the diets of social media users. We leverage the insights we gain from analysing social media users' diets to design better information discovery and exchange systems over social media.

Original languageEnglish
Title of host publicationProceedings of the 19th ACM Conference on Computer Supported Cooperative Work and Social Computing Companion, CSCW 2016 Companion
PublisherACM
Pages159-162
Number of pages4
ISBN (Electronic)9781450339506
DOIs
Publication statusPublished - 27 Feb 2016
MoE publication typeA4 Conference publication
EventACM Conference on Computer-Supported Cooperative Work and Social Computing - San Francisco, United States
Duration: 27 Feb 20162 Mar 2016
Conference number: 19

Conference

ConferenceACM Conference on Computer-Supported Cooperative Work and Social Computing
Abbreviated titleCSCW
Country/TerritoryUnited States
CitySan Francisco
Period27/02/201602/03/2016

Keywords

  • Information diets
  • Search and recommendation systems
  • Social media
  • Topic inference for Tweets

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