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Influence of controlled and uncontrolled interventions on Twitter in different target groups

Published: 25 August 2013 Publication History

Abstract

In this paper the influence of interventions on Twitter users is studied. We define influence in a) number of participants, b) size of the audience, c) amount of activity, and d) reach. Influence is studied for four different target groups: a) politicians, b) journalists, c) employees and d) the general public. Furthermore, two types of interventions are studied: a) by all Twitter users (i.e., uncontrolled interventions), and b) those tweeted by an organization that benefits from any resulting influence (i.e., controlled interventions). As a case study, tweets about a large Dutch governmental organization are used.
Results show a clear relation between the number of uncontrolled interventions and influence in all four target groups, for each of the defined types of influence. Controlled interventions show less influence: Significant influence was found for the general public, but influence for politicians and employees was only mildly significant, and no influence was found for journalists. The effect found for uncontrolled interventions however suggests that this influence is indeed reachable for some target groups, even when the number of interventions is small, and very well reachable for all target groups, provided the number of interventions is large enough.
In addition to this we found that interventions influence groups to a different extent. Own employees were influenced strongest, differing significantly from the other groups.

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cover image ACM Conferences
ASONAM '13: Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
August 2013
1558 pages
ISBN:9781450322409
DOI:10.1145/2492517
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 25 August 2013

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  1. Twitter
  2. social influence
  3. social media

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ASONAM '13
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ASONAM '13: Advances in Social Networks Analysis and Mining 2013
August 25 - 28, 2013
Ontario, Niagara, Canada

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