Mo’ characters mo’ problems: Online social media platform constraints and modes of communication
Date
2020
Authors
Mitchell, L.
Dent, J.
Ross, J.
Editors
Advisors
Journal Title
Journal ISSN
Volume Title
Type:
Conference paper
Citation
AoIRS Selected Papers of Internet Research, 2020, vol.2018, pp.10497-1-10497-5
Statement of Responsibility
Lewis Mitchell, Joshua Dent, Joshua V Ross
Conference Name
Annual Conference of the Association of Internet Researchers (AOIR) (10 Oct 2018 - 13 Oct 2018 : Montreal, Canada)
Abstract
It is widely accepted that different online social media platforms produce different modes of communication, however the ways in which these modalities are shaped by the constraints of a particular platform remain difficult to quantify. On 7 November 2017 Twitter doubled the character limit for users to 280 characters, presenting a unique opportunity to study the response of this population to an exogenous change to the communication medium. Here we analyse a large dataset comprising 387 million English-language tweets (10% of all public tweets) collected over the September 2017--January 2018 period to quantify and explain large-scale changes in individual behaviour and communication patterns precipitated by the character-length change. Using statistical and natural language processing techniques we find that linguistic complexity increased after the change, with individuals writing at a significantly higher reading level. However, we find that some textual properties such as statistical language distribution remain invariant across the change, and are no different to writings in different online media. By fitting a generative mathematical model to the data we find a surprisingly slow response of the Twitter population to this exogenous change, with a substantial number of users taking a number of weeks to adjust to the new medium. In the talk we describe the model and Bayesian parameter estimation techniques used to make these inferences. Furthermore, we argue for mathematical models as an alternative exploratory methodology for "Big" social media datasets, empowering the researcher to make inferences about the human behavioural processes which underlie large-scale patterns and trends.
School/Discipline
Dissertation Note
Provenance
Description
Paper presented at AoIR 2018: The 19thAnnual Conference of the Association of Internet Researchers. Montréal, Canada: AoIR.
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