Authentic chatter


This operations research aims to derive an easy but meaningful method for practitioners to identify key influencers and uncover suppressed narratives within a Twitter topic group. This research employs a new concept called “authentic chatter” (analogous to a grass-roots discourse) in combination with influence metrics, content analysis, and commercial-off-the-shelf social media analysis software (NexaIntelligence). The mixed-method exploits the power of social network analysis to determine a small but prominent group of influencers that provides a manageable dataset for the qualitative review of the content. This paper reviews research on social influence and identifies two local influence theories, “indegree” and “retweet”, ideal for topical discussion. Next it reviews Twitter content analysis research looking at specific details on methods. Findings from this past research guide development of a new methodology. The research concludes that use of a prominent group and filtering for authentic chatter increased the signal to noise ratio highlighting important underlying themes within the topic.

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  1. 1.

    The idea and formal definition of a prominent group is discussed in (Kardara et al. 2015). In brief she states: “the members of a topic community typically differ in the degree of influence they exert over their peers. Some users are rather passive, while others excel in some aspect of the community, affecting the behavior of other members and setting relevant trends. We call influencers or prominent users those members that have established a prominent position inside a community. Collectively, the influential users of a specific community comprise a sub-community that is called prominent group.”

  2. 2.

    In this case “local influence” means influence that is felt within a particular topic area, or # within Twitter. A person may be considered an expert in that particular domain but not so outside that topic area.

  3. 3.

    NexaIntelligence by Nexalogy was used in this research.

  4. 4.

    Note a person who creates a tweet can be referred to as a user, publisher, actor or author within this paper.

  5. 5.

    LIWC—Linguistic Inquiry and Word Count is a text analysis algorithm that exposes emotional, cognitive, and structural components present in text collections. See for more details.

  6. 6.

    Note the colours with Fig. 1 represent outliers, are produced automatically by the software, but were not used for this research.


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Correspondence to Bruce Forrester.

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Forrester, B. Authentic chatter. Comput Math Organ Theory 26, 382–411 (2020).

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  • Authentic chatter
  • Social network analysis
  • Content analysis
  • Mixed-method
  • Twitter analysis