short-paper

Towards Understanding Political Interactions on Instagram

Authors:

Ana Paula Couto da Silva,

Jussara M. AlmeidaAuthors Info & Claims

HT '19: Proceedings of the 30th ACM Conference on Hypertext and Social Media

Pages 247 - 251

https://doi.org/10.1145/3342220.3343657

Published: 12 September 2019 Publication History

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Abstract

Online Social Networks (OSNs) allow personalities and companies to communicate directly with the public, bypassing filters of traditional medias. As people rely on OSNs to stay up-to-date, the political debate has moved online too. We witness the sudden explosion of harsh political debates and the dissemination of rumours in OSNs. Identifying such behaviour requires a deep understaning on how people interact via OSNs during political debates. We present a preliminary study of interactions in a popular OSN, namely Instagram. We take Italy as a case study in the period before the 2019 European Elections. We observe the activity of top Italian Instagram profiles in different categories: politics, music, sport and show. We record their posts for more than two months, tracking "likes'' and comments from users. Results suggest that profiles of politicians attract markedly different interactions than other categories. People tend to comment more, with longer comments, debating for longer time, with a large number of replies, most of which are not explicitly solicited. Moreover, comments tend to come from a small group of very active users. Finally, we witness substantial differences when comparing profiles of different parties.

References

[1]

Eddy Borges-Rey. 2015. News Images on Instagram: The paradox of authenticity in hyperreal photo reportage. Digital Journalism, Vol. 3, 4 (2015), 571--593.

Crossref

Google Scholar

[2]

Martin Gibbs, James Meese, Michael Arnold, Bjorn Nansen, and Marcus Carter. 2015. # Funeral and Instagram: death, social media, and platform vernacular. Information, Communication & Society, Vol. 18, 3 (2015), 255--268.

Crossref

Google Scholar

[3]

Tim Highfield and Tama Leaver. 2015. A methodology for mapping Instagram hashtags. First Monday, Vol. 20, 1 (2015), 1--11.

Google Scholar

[4]

Philip N Howard and Bence Kollanyi. 2016. Bots,# StrongerIn, and# Brexit: computational propaganda during the UK-EU referendum. Available at SSRN 2798311 (2016).

Google Scholar

[5]

Yuheng Hu, Lydia Manikonda, and Subbarao Kambhampati. 2014. What we instagram: A first analysis of instagram photo content and user types. In Eighth International AAAI conference on weblogs and social media .

Google Scholar

[6]

Jamie Mahoney, Tom Feltwell, Obinna Ajuruchi, and Shaun Lawson. 2016. Constructing the visual online political self: an analysis of Instagram Use by the Scottish Electorate. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems. ACM, 3339--3351.

Digital Library

Google Scholar

[7]

Andrew G Reece and Christopher M Danforth. 2017. Instagram photos reveal predictive markers of depression. EPJ Data Science, Vol. 6, 1 (2017), 15.

Crossref

Google Scholar

[8]

Gustavo Resende, Philipe Melo, Hugo Sousa, Johnnatan Messias, Marisa Vasconcelos, Jussara Almeida, and Fabrício Benevenuto. 2019. (Mis)Information Dissemination in WhatsApp: Gathering, Analyzing and Countermeasures. In To appear in Proceedings of the Web Conference 2019 .

Digital Library

Google Scholar

[9]

Jessica L Ridgway and Russell B Clayton. 2016. Instagram unfiltered: Exploring associations of body image satisfaction, Instagram# selfie posting, and negative romantic relationship outcomes. Cyberpsychology, Behavior, and Social Networking, Vol. 19, 1 (2016), 2--7.

Crossref

Google Scholar

[10]

Pavica Sheldon and Katherine Bryant. 2016. Instagram: Motives for its use and relationship to narcissism and contextual age. Computers in human Behavior, Vol. 58 (2016), 89--97.

Digital Library

Google Scholar

[11]

Martino Trevisan, Danilo Giordano, Idilio Drago, Marco Mellia, and Maurizio Munafo. 2018. Five Years at the Edge: Watching Internet from the ISP Network. In Proceedings of the 14th International Conference on Emerging Networking EXperiments and Technologies (CoNEXT '18). 1--12.

Digital Library

Google Scholar

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Martins ESalles IBenevenuto FGoussevskaia OBellogín ABoratto LCena F(2022)Characterizing Sponsored Content in Facebook and InstagramProceedings of the 33rd ACM Conference on Hypertext and Social Media10.1145/3511095.3531289(52-63)Online publication date: 28-Jun-2022
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Sunarso SSetiawan BAnjani N(2022)The political satire of Mojok.Co in the 2019 Indonesian electionHeliyon10.1016/j.heliyon.2022.e10018(e10018)Online publication date: Jul-2022
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Index Terms

Towards Understanding Political Interactions on Instagram
1. Networks
  1. Network types
    1. Overlay and other logical network structures
      1. Online social networks

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Information

Published In

HT '19: Proceedings of the 30th ACM Conference on Hypertext and Social Media

September 2019

326 pages

ISBN:9781450368858

DOI:10.1145/3342220

General Chairs:
Claus Atzenbeck
Hof University, Germany
,
Jessica Rubart
OWL University of Applied Sciences and Arts, Germany
,
Program Chair:
David E. Millard
University of Southampton, UK

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 12 September 2019

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HT '19: 30th ACM Conference on Hypertext and Social Media

September 17 - 20, 2019

Hof, Germany

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Overall Acceptance Rate 378 of 1,158 submissions, 33%

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https://dl.acm.org/doi/10.1145/3570748.3570753
Martins ESalles IBenevenuto FGoussevskaia OBellogín ABoratto LCena F(2022)Characterizing Sponsored Content in Facebook and InstagramProceedings of the 33rd ACM Conference on Hypertext and Social Media10.1145/3511095.3531289(52-63)Online publication date: 28-Jun-2022
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Sunarso SSetiawan BAnjani N(2022)The political satire of Mojok.Co in the 2019 Indonesian electionHeliyon10.1016/j.heliyon.2022.e10018(e10018)Online publication date: Jul-2022
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Kang JYoon JPark EHan J(2022)“Why tag me?”Expert Systems with Applications: An International Journal10.1016/j.eswa.2022.117171202:COnline publication date: 15-Sep-2022
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#mytweet via Instagram: Exploring User Behaviour across Multiple Social Networks

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