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A Twitter-based approach of news media impartiality in multipartite political scenes

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Abstract

News dissemination in the modern world deploys online social networks (OSNs) to instantly and freely convey facts and opinions to Internet users worldwide. Recent research studies the structure of the graph formed by the relationships between news readers and media outlets in OSNs to investigate the profile of the media and derive their political leanings. In this work, we focus on the notion of political impartiality in multipartite political scenes. Our aim is to describe the graph-theoretic attributes of the ideal outlet that exhibits an impartial stance towards all political groups and propose a methodology based on Twitter, an OSN with profound informative and political profile, to algorithmically approximate this ideal medium and evaluate the deviation of popular outlets from it. The magnitude of deviation is used to rank the existing outlets based on their political impartiality and, hence, tackle the bewildering question: Which are the most impartial news media in a political scene?. We utilize our techniques on a snapshot of the Twitter subgraph concerning the Greek political and news media scene in April 2018. The results of our approach are juxtaposed with the findings of a survey provided to a group of political scientists and the efficiency of our proposed methodology is soundly confirmed.

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Notes

  1. https://www.hellenicparliament.gr/en/Vouleftes/Ana-Koinovouleftiki-Omada/, accessed December 2017.

  2. https://figshare.com/s/af00b25a1b408888267a

References

  • An J, Cha M, Gummadi K, Crowcroft J, Quercia D (2012) Visualizing media bias through Twitter. In: 6th international AAAI conference on weblogs and social media (ICWSM 2012), workshop on the potential of social media tools and data for journalists, pp 2–5

  • Barberá P (2015) Birds of the same feather tweet together: Bayesian ideal point estimation using Twitter data. Polit Anal 23(1):76–91. https://doi.org/10.1093/pan/mpu011

    Article  Google Scholar 

  • Bhatt S, Joglekar S, Bano S, Sastry N (2018) Illuminating an ecosystem of partisan websites. In: Companion proceedings of the web conference 2018, international world wide web conferences steering committee, Republic and Canton of Geneva, CHE, WWW’18, pp 545–554. https://doi.org/10.1145/3184558.3188725

  • Blumler JG, Gurevitch M (1995) The crisis of public communication. Routledge, London

    Google Scholar 

  • Boutet A, Kim H, Yoneki E (2013) What’s in Twitter, I know what parties are popular and who you are supporting now!. Soc Netw Anal Min 3(4):1379–1391. https://doi.org/10.1007/s13278-013-0120-1

    Article  Google Scholar 

  • Brandenburg H (2006) Party strategy and media bias: a quantitative analysis of the 2005 UK election campaign. J Elect Public Opin Parties 16(2):157–178. https://doi.org/10.1080/13689880600716027

    Article  Google Scholar 

  • Conover M, Ratkiewicz J, Francisco M, Goncalves B, Menczer F, Flammini A (2011) Political polarization on Twitter. In: 5th international AAAI conference on weblogs and social media (ICWSM 2011), pp 89–96

  • Degroot MH (1974) Reaching a consensus. J Am Stat Assoc 69(345):118–121. https://doi.org/10.1080/01621459.1974.10480137

    Article  MATH  Google Scholar 

  • Garimella K, Morales GDF, Gionis A, Mathioudakis M (2018) Quantifying controversy on social media. Trans Soc Comput 1(1). https://doi.org/10.1145/3140565

  • Ghaderi J, Srikant R (2013) Opinion dynamics in social networks: a local interaction game with stubborn agents. In: 2013 American control conference, pp 1982–1987. https://doi.org/10.1109/ACC.2013.6580126

  • Golbeck J, Hansen D (2014) A method for computing political preference among Twitter followers. Soc. Netw. 36:177–184. https://doi.org/10.1016/j.socnet.2013.07.004

    Article  Google Scholar 

  • Gyftopoulos S, Efraimidis PS, Katsaros P (2018) Formal analysis of Degroot influence problems using probabilistic model checking. Simul Model Pract Theory 89:144–159

    Article  Google Scholar 

  • Hallin DC, Mancini P (2004) Comparing media systems: three models of media and politics. Commun. Soc. Polit. https://doi.org/10.1017/CBO9780511790867

    Article  Google Scholar 

  • Hanawa K, Sasaki S, Ouchi H, Suzuki J, Inui K (2019) The Sally Smedley hyperpartisan news detector at SemEval-2019 task 4. In: Proceedings of the 13th international workshop on semantic evaluation. Association for Computational Linguistics. Minneapolis, MN, USA, pp 1057–1061. https://doi.org/10.18653/v1/S19-2185

  • Hassanpour N (2010) Dynamic models of mobilization in political networks. In: Proceedings of the 2010 political networks conference

  • Iosifidis P, Boucas D (2015) Media policy and independent journalism in Greece. Open Society Foundations. https://www.opensocietyfoundations.org/publications/media-policy-and-independent-journalism-greece

  • Jiang Y, Petrak J, Song X, Bontcheva K, Maynard D (2019) Team bertha von Suttner at SemEval-2019 task 4: hyperpartisan news detection using ELMo sentence representation convolutional network. In: Proceedings of the 13th international workshop on semantic evaluation. Association for Computational Linguistics, Minneapolis, MN, USA, pp 840–844. https://doi.org/10.18653/v1/S19-2146

  • Kiesel J, Mestre M, Shukla R, Vincent E, Adineh P, Corney D, Stein B, Potthast M (2019) SemEval-2019 task 4: hyperpartisan news detection. In: Proceedings of the 13th international workshop on semantic evaluation. Association for Computational Linguistics, Minneapolis, MN, USA, pp 829–839. https://doi.org/10.18653/v1/S19-2145

  • Kwak H, Lee C, Park H, Moon S (2010) What is Twitter, a social network or a news media? In: Proceedings of the 19th international conference on world wide web (WWW ’10). ACM, New York, NY, USA, pp 591–600. https://doi.org/10.1145/1772690.1772751

  • Kwiatkowska M, Norman G, Parker D (2011) PRISM 4.0: verification of probabilistic real-time systems. In: Gopalakrishnan G, Qadeer S (eds) Proceedings of the 23rd international conference on computer aided verification (CAV’11), LNCS, vol 6806. Springer, pp 585–591

  • Le H, Shafiq Z, Srinivasan P (2017) Scalable news slant measurement using Twitter. In: 11th international AAAI conference on web and social media (ICWSM 2017), pp 584–587

  • Leys C, Ley C, Klein O, Bernard P, Licata L (2013) Detecting outliers: do not use standard deviation around the mean, use absolute deviation around the median. J Exp Soc Psychol 49(4):764–766. https://doi.org/10.1016/j.jesp.2013.03.013

    Article  Google Scholar 

  • Los Angeles Times (2011) Los Angeles times ethics guidelines (Online). https://latimesblogs.latimes.com/readers/2011/02/la-times-ethics-guidelines.html. Accessed 5 Aug 2019

  • Makazhanov A, Rafiei D, Waqar M (2014) Predicting political preference of Twitter users. Soc Netw Anal Min 4(1):193. https://doi.org/10.1007/s13278-014-0193-5

    Article  Google Scholar 

  • Marozzo F, Bessi A (2017) Analyzing polarization of social media users and news sites during political campaigns. Soc Netw Anal Min 8(1):1. https://doi.org/10.1007/s13278-017-0479-5

    Article  Google Scholar 

  • Mazor J (2018) Media impartiality, part 1: what, when, and why (online). http://www.lse.ac.uk/philosophy/blog/2018/08/14/media-impartiality-1/. Accessed 5 Aug 2019

  • McQuail D (1992) Media performance: mass communication and the public interest, vol 144. Sage, London

    Google Scholar 

  • Morales AJ, Borondo J, Losada JC, Benito RM (2015) Measuring political polarization: Twitter shows the two sides of Venezuela. Chaos: Interdiscip J Nonlinear Sci 25(3):033114. https://doi.org/10.1063/1.4913758

  • Pennacchiotti M, Popescu AM (2011a) Democrats, republicans and starbucks afficionados: user classification in Twitter. In: Proceedings of the 17th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, New York, NY, USA, KDD ’11, pp 430–438. https://doi.org/10.1145/2020408.2020477

  • Pennacchiotti M, Popescu AM (2011b) A machine learning approach to Twitter user classification. In: 5th international AAAI conference on weblogs and social media (ICWSM 2011), pp 281–288

  • Pew Research Center (2018) Publics globally want unbiased news coverage, but are divided on whether their news media deliver. https://www.pewresearch.org/global/2018/01/11/publics-globally-want-unbiased-news-coverage-but-are-divided-on-whether-their-news-media-deliver/

  • Rao D, Yarowsky D, Shreevats A, Gupta M (2010) Classifying latent user attributes in Twitter. In: Proceedings of the 2nd international workshop on search and mining user-generated contents (SMUC ’10). ACM, New York, NY, USA, pp 37–44. https://doi.org/10.1145/1871985.1871993

  • Sainudiin R, Yogeeswaran K, Nash K, Sahioun R (2019) Characterizing the Twitter network of prominent politicians and SPLC-defined hate groups in the 2016 US presidential election. Soc Netw Anal Min 9(1):34. https://doi.org/10.1007/s13278-019-0567-9

    Article  Google Scholar 

  • Seymour-Ure C (1974) The political impact of mass media, vol 4. Constable, London; Sage Publications, Beverly Hills, CA

  • Srivastava V, Gupta A, Prakash D, Sahoo SK, RR R, Kim YH (2019) Vernon-fenwick at SemEval-2019 task 4: hyperpartisan news detection using lexical and semantic features. In: Proceedings of the 13th international workshop on semantic evaluation. Association for Computational Linguistics, Minneapolis, MA, USA, pp 1078–1082. https://doi.org/10.18653/v1/S19-2189

  • Stamatelatos G, Gyftopoulos S, Drosatos G, Efraimidis PS (2018) Deriving the political affinity of Twitter users from their followers. In: 11th IEEE International conference on social computing and networking (SocialCom 2018). IEEE, pp 1175–1182. https://doi.org/10.1109/BDCloud.2018.00173

  • Stamatelatos G, Gyftopoulos S, Drosatos G, Efraimidis PS (2020) Revealing the political affinity of online entities through their twitter followers. Inf. Process. Manag. 57(2):102172. https://doi.org/10.1016/j.ipm.2019.102172

    Article  Google Scholar 

  • Stieglitz S, Dang-Xuan L (2013) Social media and political communication: a social media analytics framework. Soc Netw Anal Min 3(4):1277–1291. https://doi.org/10.1007/s13278-012-0079-3

    Article  Google Scholar 

  • Tumasjan A, Sprenger TO, Sandner PG, Welpe IM (2010) Predicting elections with Twitter: What 140 characters reveal about political sentiment. In: 4th international AAAI conference on weblogs and social media (ICWSM 2010), pp 178–185

  • Tumasjan A, Sprenger TO, Sandner PG, Welpe IM (2011) Election forecasts with Twitter: how 140 characters reflect the political landscape. Soc Sci Comput Rev 29(4):402–418. https://doi.org/10.1177/0894439310386557

    Article  Google Scholar 

  • Van Kempen H (2007) Media-party parallelism and its effects: a cross-national comparative study. Politi. Commun. 24(3):303–320. https://doi.org/10.1080/10584600701471674

    Article  Google Scholar 

  • Ward MD, Stovel K, Sacks A (2011) Network analysis and political science. Annu Rev Polit Sci 14(1):245–264. https://doi.org/10.1146/annurev.polisci.12.040907.115949

    Article  Google Scholar 

  • Wong FMF, Tan CW, Sen S, Chiang M (2016) Quantifying political leaning from tweets, retweets, and retweeters. IEEE Trans Knowl Data Eng 28(8):2158–2172. https://doi.org/10.1109/TKDE.2016.2553667

    Article  Google Scholar 

  • Zamal FA, Liu W, Ruths D (2012) Homophily and latent attribute inference: Inferring latent attributes of twitter users from neighbors. In: 6th international AAAI Conference on Web and Social Media

Download references

Acknowledgements

This work was supported by the project “Assessment of News Reliability in Social Networks of Influence” (Grant No. MIS 5006337) that has been co-financed by the Operational Program “Human Resources Development, Education and Lifelong Learning” and is co-financed by the European Union (European Social Fund) and Greek National funds. We thank Chrysanthos Tassis and Costas Eleftheriou from the Department of Social Administration and Political Science, Democritus University of Thrace, for their support in running the survey and Maria Kyriakidou from the School of Journalism, Media and Culture, Cardiff University, for the valuable information and guidance in comprehending the notion of news media impartiality. We would also like to thank the anonymous reviewer for providing the valuable feedback that led us to the creation of projection \(Proj^{G}_{\phi -pos}\).

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Democritus University of Thrace, 67100, Kimmeria, Xanthi, Greece

    Sotirios Gyftopoulos, Giorgos Stamatelatos & Pavlos S. Efraimidis

  2. Institute for Language and Speech Processing, Athena Research Centre, 67100, Xanthi, Greece

    George Drosatos

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  1. Sotirios Gyftopoulos
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  4. Pavlos S. Efraimidis
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Correspondence to Sotirios Gyftopoulos.

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Gyftopoulos, S., Drosatos, G., Stamatelatos, G. et al. A Twitter-based approach of news media impartiality in multipartite political scenes. Soc. Netw. Anal. Min. 10, 36 (2020). https://doi.org/10.1007/s13278-020-00642-x

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