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L’analyse des opinions politiques sur Twitter: Défis et opportunités d’une approche multi-échelle

Authors: Severo, Marta; Lamarche-Perrin, Robin;

L’analyse des opinions politiques sur Twitter: Défis et opportunités d’une approche multi-échelle

Abstract

Social research on public opinion has been affected by the recent deluge of new digital data on the Web, from blogs and forums to Facebook pages and Twitter accounts. This fresh type of information useful for mining opinions is emerging as an alternative to traditional techniques, such as opinion polls. Firstly, by building the state of the art of studies of political opinion based on Twitter data, this paper aims at identifying the relationship between the chosen data analysis method and the definition of political opinion implied in these studies. Secondly, it aims at investigating the feasibility of performing multiscale analysis in digital social research on political opinion by addressing the merits of several methodological techniques, from content-based to interaction-based methods, from statistical to semantic analysis, from supervised to unsupervised approaches. The end result of such an approach is to identify future trends in social science research on political opinion.; Des blogs et forums aux pages Facebook et comptes Twitter, le récent déluge des données numériques du Web a fortement affecté la recherche en sciences sociales. Cette nouvelle catégorie d’information, utile à l’extraction des opinions politiques, se présente comme une alternative aux techniques traditionnelles telles que les sondages. Premièrement, en réalisant un état de l’art des études de l’opinion s’appuyant sur les données Twitter, cet article vise à mettre en relation les méthodes d’analyse utilisées dans ces études et les définitions de l’opinion politique qui y sont suggérées. Deuxièmement, cet article étudie la faisabilité de réaliser des analyses multi-échelles en sciences sociales concernant l’étude de l’opinion politique en exposant les mérites de plusieurs méthodes, allant des méthodes orientées contenus aux méthodes orientées interactions, de l’analyse statistique à l’analyse sémantique, des approches supervisées aux approches non-supervisées. Le résultat de notre démarche est d’ainsi identifier les tendances futures de la recherche en sciences sociales concernant l’étude de l’opinion politique.

Country
France
Related Organizations
Keywords

[SHS.SOCIO]Humanities and Social Sciences/Sociology, [SHS.STAT]Humanities and Social Sciences/Methods and statistics, Twitter, Réseaux sociaux, Analyse multi-échelle, Political opinion, Opinion politique, Quali-quantitative approaches, Opinion mining, Social media, Big data, [INFO.INFO-CY]Computer Science [cs]/Computers and Society [cs.CY], Extraction d'opinions, Multiscale analysis, Approches quali-quantitatives

EOSC Subjects

Twitter Data

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ANDERSON C., 2008, « The End of Theory: The Data Deluge Makes the Scientific Method Obsolete », Wired, 16:7.

BARBERÁ P., JOST J. T., NAGLER J., TUCKER J. A., and BONNEAU R., 2015, « Tweeting From Left to Right: Is Online Political Communication More Than an Echo Chamber? » Psychological Science, 26:10, p. 1531-1542.

BASTOS, M. T., RAIMUNDO, R. L. G., and TRAVITZKI, R., 2013, « Gatekeeping Twitter: message diffusion in political hashtags », Media, Culture & Society, 35, 2, p. 260-270.

SEVERO M., FEREDJ A., and ROMELE A., 2016, « Soft Data and Public Policy: Can Social Media Offer Alternatives to Official Statistics in Urban Policymaking? », Policy & Internet, 8, 3, p. 354-372.

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
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