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Human content filtering in Twitter: The influence of metadata

Chorley, Martin J. ORCID:, Colombo, Gualtiero B., Allen, Stuart M. ORCID: and Whitaker, Roger M. ORCID: 2015. Human content filtering in Twitter: The influence of metadata. International Journal of Human-Computer Studies 74 , pp. 32-40. 10.1016/j.ijhcs.2014.10.001

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Social micro-blogging systems such as Twitter are designed for rapid and informal communication from a large potential number of participants. Due to the volume of content received, human users must typically skim their timeline of received content and exercise judgement in selecting items for consumption, necessitating a selection process based on heuristics and content meta-data. This selection process is not well understood, yet is important due to its potential use in content management systems. In this research we have conducted an open online experiment in which participants are shown quantitative and qualitative meta-data describing two pieces of Twitter content. Without revealing the text of the tweet, participants are asked to make a selection. We observe the decisions made from 239 surveys and discover insights into human behaviour on decision making for content selection. We find that for qualitative meta-data consumption decisions are driven by online friendship and for quantitative meta-data the largest numerical value presented influences choice. Overall, the ‘number of retweets’ is found to be the most influential quantitative meta-data, while displaying multiple cues about an author's identity provides the strongest qualitative meta-data. When both quantitative and qualitative meta-data is presented, it is the qualitative meta-data (friendship information) that drives selection. The results are consistent with application of the Recognition heuristic, which postulates that when faced with constrained decision-making, humans will tend to exercise judgement based on cues representing familiarity. These findings are useful for future interface design for content filtering and recommendation systems.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Systems Immunity Research Institute (SIURI)
Subjects: Q Science > QA Mathematics > QA76 Computer software
T Technology > T Technology (General)
Additional Information: This is an open access article under the CCBY license
Publisher: Elsevier
ISSN: 1071-5819
Funders: EPSRC, EC
Date of First Compliant Deposit: 30 March 2016
Date of Acceptance: 1 October 2014
Last Modified: 17 May 2023 00:36

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