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XRDS Crossroads The ACM Magazine for Students
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Detecting influencers in social media discussions

descriptionPublicationkeyboard_double_arrow_right Article , Other literature type , Doctoral thesis , Thesis 14 Oct 2014 United States English Publisher:Association for Computing Machinery (ACM)Journal:XRDS: Crossroads, The ACM Magazine for Students, volume 21, pages 40-45 (issn: 1528-4972, eissn: 1528-4980, Copyright policy )
Authors: Rosenthal, Sara;

doi: 10.1145/2659889 , 10.7916/d8d50m6m

Detecting influencers in social media discussions

Abstract

Knowing who's influential can help when planning political campaigns, advertising strategies, or even combating terrorism; and now research into influence detection promises to automate such detection.

Country
United States
Related Organizations
Keywords

Sociolinguistics, Computer science, 004

  • BIP!
    Impact byBIP!
    selected citations
    These citations are derived from selected sources.
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    		 8
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    		 Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    		 Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    		 Average
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
8
Top 10%
Average
Average
Green
bronze
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