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      Putting the methodological brakes on claims to measure national happiness through Twitter: Methodological limitations in social media analytics

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      Journal: PLoS ONE
      Publisher: Public Library of Science

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          Abstract

          With the rapid global proliferation of social media, there has been growing interest in using this existing source of easily accessible ‘big data’ to develop social science knowledge. However, amidst the big data gold rush, it is important that long-established principles of good social research are not ignored. This article critically evaluates Mitchell et al.’s (2013) study, ‘The Geography of Happiness: Connecting Twitter Sentiment and Expression, Demographics, and Objective Characteristics of Place’, demonstrating the importance of attending to key methodological issues associated with secondary data analysis.

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          The Geography of Happiness: Connecting Twitter sentiment and expression, demographics, and objective characteristics of place

          Lewis Mitchell, Kameron Decker Harris, Morgan Frank … (2013)
          We conduct a detailed investigation of correlations between real-time expressions of individuals made across the United States and a wide range of emotional, geographic, demographic, and health characteristics. We do so by combining (1) a massive, geo-tagged data set comprising over 80 million words generated over the course of several recent years on the social network service Twitter and (2) annually-surveyed characteristics of all 50 states and close to 400 urban populations. Among many results, we generate taxonomies of states and cities based on their similarities in word use; estimate the happiness levels of states and cities; correlate highly-resolved demographic characteristics with happiness levels; and connect word choice and message length with urban characteristics such as education levels and obesity rates. Our results show how social media may potentially be used to estimate real-time levels and changes in population-level measures such as obesity rates.
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            Composition in distributional models of semantics.

            Jeff Mitchell, Mirella Lapata (2010)
            Vector-based models of word meaning have become increasingly popular in cognitive science. The appeal of these models lies in their ability to represent meaning simply by using distributional information under the assumption that words occurring within similar contexts are semantically similar. Despite their widespread use, vector-based models are typically directed at representing words in isolation, and methods for constructing representations for phrases or sentences have received little attention in the literature. This is in marked contrast to experimental evidence (e.g., in sentential priming) suggesting that semantic similarity is more complex than simply a relation between isolated words. This article proposes a framework for representing the meaning of word combinations in vector space. Central to our approach is vector composition, which we operationalize in terms of additive and multiplicative functions. Under this framework, we introduce a wide range of composition models that we evaluate empirically on a phrase similarity task.
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              Who Tweets? Deriving the Demographic Characteristics of Age, Occupation and Social Class from Twitter User Meta-Data

              Luke Sloan, Jeffrey Morgan, Pete Burnap … (2015)
              This paper specifies, designs and critically evaluates two tools for the automated identification of demographic data (age, occupation and social class) from the profile descriptions of Twitter users in the United Kingdom (UK). Meta-data data routinely collected through the Collaborative Social Media Observatory (COSMOS: http://www.cosmosproject.net/) relating to UK Twitter users is matched with the occupational lookup tables between job and social class provided by the Office for National Statistics (ONS) using SOC2010. Using expert human validation, the validity and reliability of the automated matching process is critically assessed and a prospective class distribution of UK Twitter users is offered with 2011 Census baseline comparisons. The pattern matching rules for identifying age are explained and enacted following a discussion on how to minimise false positives. The age distribution of Twitter users, as identified using the tool, is presented alongside the age distribution of the UK population from the 2011 Census. The automated occupation detection tool reliably identifies certain occupational groups, such as professionals, for which job titles cannot be confused with hobbies or are used in common parlance within alternative contexts. An alternative explanation on the prevalence of hobbies is that the creative sector is overrepresented on Twitter compared to 2011 Census data. The age detection tool illustrates the youthfulness of Twitter users compared to the general UK population as of the 2011 Census according to proportions, but projections demonstrate that there is still potentially a large number of older platform users. It is possible to detect “signatures” of both occupation and age from Twitter meta-data with varying degrees of accuracy (particularly dependent on occupational groups) but further confirmatory work is needed.
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                Author and article information

                Contributors
                Johan Bollen: Role: Editor
                Journal
                Journal ID (nlm-ta): PLoS One
                Journal ID (iso-abbrev): PLoS ONE
                Journal ID (publisher-id): plos
                Journal ID (pmc): plosone
                Title: PLoS ONE
                Publisher: Public Library of Science (San Francisco, CA USA )
                ISSN (Electronic): 1932-6203
                Publication date (Electronic): 7 September 2017
                Publication date Collection: 2017
                Volume: 12
                Issue: 9
                Electronic Location Identifier: e0180080
                Affiliations
                [001]Department of Sociology, University of Warwick, Coventry, United Kingdom
                Indiana University Bloomington, UNITED STATES
                Author notes

                Competing Interests: The author has declared that no competing interests exist.

                • Conceptualization: EJ.

                • Formal analysis: EJ.

                • Investigation: EJ.

                • Methodology: EJ.

                • Project administration: EJ.

                • Resources: EJ.

                • Writing – original draft: EJ.

                • Writing – review & editing: EJ.

                Article
                Publisher ID: PONE-D-14-45765
                DOI: 10.1371/journal.pone.0180080
                PMC ID: 5589095
                PubMed ID: 28880882
                SO-VID: 92b89a04-eaeb-4805-9622-1908cacc3104
                Copyright © © 2017 Eric Allen Jensen
                License:

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                Date received : 12 October 2014
                Date accepted : 22 May 2017
                Page count
                Figures: 0, Tables: 0, Pages: 7
                Funding
                The author received no specific funding for this work.
                Categories
                Subject: Formal Comment
                Subject: Social Sciences
                Subject: Sociology
                Subject: Communications
                Subject: Social Communication
                Subject: Social Media
                Subject: Twitter
                Subject: Computer and Information Sciences
                Subject: Network Analysis
                Subject: Social Networks
                Subject: Social Media
                Subject: Twitter
                Subject: Social Sciences
                Subject: Sociology
                Subject: Social Networks
                Subject: Social Media
                Subject: Twitter
                Subject: Biology and Life Sciences
                Subject: Psychology
                Subject: Emotions
                Subject: Happiness
                Subject: Social Sciences
                Subject: Psychology
                Subject: Emotions
                Subject: Happiness
                Subject: Social Sciences
                Subject: Sociology
                Subject: Communications
                Subject: Social Communication
                Subject: Social Media
                Subject: Computer and Information Sciences
                Subject: Network Analysis
                Subject: Social Networks
                Subject: Social Media
                Subject: Social Sciences
                Subject: Sociology
                Subject: Social Networks
                Subject: Social Media
                Subject: Social Sciences
                Subject: Sociology
                Subject: Social Research
                Subject: People and Places
                Subject: Demography
                Subject: Biology and Life Sciences
                Subject: Behavior
                Subject: Social Sciences
                Subject: Earth Sciences
                Subject: Geography
                Subject: Human Geography
                Subject: Behavioral Geography
                Subject: Social Sciences
                Subject: Human Geography
                Subject: Behavioral Geography

                ScienceOpen disciplines: Uncategorized
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                ScienceOpen disciplines: Uncategorized

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