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Microblog Analysis as a Program of Work

Tolmie, Peter, Procter, Rob, Rouncefield, Mark, Liakata, Maria, Zubiaga, Arkaitz·2018·ACM Transactions on Social Computing
DOI 10.1145/3162956
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Abstract

Inspired by a European project, PHEME, that requires the close analysis of Twitter-based conversations in order to look at the spread of rumors via social media, this article has two objectives. The first of these is to take the analysis of microblogs back to first principles and lay out what microblog analysis should look like as a foundational program of work. The other is to describe how this is of fundamental relevance to human-computer interaction's interest in grasping the constitution of people's interactions with technology within the social order. Our critical finding is that, despite some surface similarities, Twitter-based conversations are a wholly distinct social phenomenon requiring an independent analysis that treats them as unique phenomena in their own right, rather than as another species of conversation that can be handled within the framework of existing conversation analysis. This motivates the argument that microblog analysis be established as a foundationally independent program, examining the organizational characteristics of microblogging from the ground up. We articulate how aspects of this approach have already begun to shape our design activities within the PHEME project.

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  • When no authoritative statement confirms or denies a rumour, social media users often share their own views on its truth through collective sense-making, which can expose the truth.

    “In the absence of an authoritative statement corroborating or debunking an ongoing rumour, it is observed that social media users will often share their own thoughts on its veracity via a process of collective, inter-subjective sense-making”

    From Detection and Resolution of Rumours in Social Media (2019) · cites this paper as Tolmie et al. 2018

    Section 6 (page unavailable) describes Twitter users responding to a rumour, agreeing or disagreeing with posts, and collectively identifying a possible confusion between two events. It does not establish that this often happens specifically when no authoritative statement is available, or that the discussion itself exposes the truth; the example includes a user citing a Canadian TV report. (LLM, conf=0.81)

    Experimental — accuracy not yet measured

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Cited by

Sorted by citations

Authors
Tolmie, P., Procter, R., Rouncefield, M., Liakata, M., & Zubiaga, A.
Affiliations
  • Universität Siegen, Siegen, Germany
  • University of Warwick, Coventry, United Kingdom
  • Lancaster University, Lancaster, United Kingdom
Venue
ACM Transactions on Social Computing
Publisher
Association for Computing Machinery (ACM)
Published
2018-01-18
Volume
1
Issue
1
Pages
1-40
ISSN
  • 2469-7818
  • 2469-7826
DOI
10.1145/3162956
Cited by
14
References
2
Language
en
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor)
Funders
EC FP7-ICT Collaborative Project PHEME (611233)

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