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Monitoring of The Last Us Presidential Elections


Affiliations
1 Department of IFAAR Institute, Bern, Switzerland
2 Computer Science Dept., University of Neuchatel, Switzerland
 

This paper presents the results of a new monitoring project of the US presidential elections with the aim of establishing computer-based tools to track in real time the popularity or awareness of candidates. The designed and developed innovative methods allow us to extract the frequency of queries sent to numerous search engines by US Internet users. Based on these data, this paper demonstrates that Trump was more frequently searched than the Democratic candidates, either Hillary Clinton in 2016 or Joe Biden in 2020. When analyzing the topics, it is observed that in 2020 the US users had shown a remarkable interest in two subjects, namely, Coronavirus and Jobs (unemployment). Interest for other topics such as Education or Healthcare were less pronounced while issues such as Immigration were given even less attention by users. Finally, some “flame” topics such as Black Lives Matter (2020) and Gun Control (2016) appear to be very popular for a few weeks before returning to a low level of interest. When analyzing tweets sent by candidates during the 2020 campaign, one can observe that Trump was focused mainly on Jobs and on Riots, announcing what would happen if Democrats took power. To these negative ads, Biden answered by putting forward moral values (e.g., love, honesty) and political symbols (e.g., democracy, rights) and by underlying the failure of the current administration in resolving the pandemic situation.

Keywords

E-Government, Online Searches, Search Engines, Discourse Analysis, Political Science.
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  • Graham, S., Milligan, I., & Weingart, S. 2016. Exploring Big Historical Data. The Historian’s Macroscope. Imperial College Press, London (UK).

  • Brin, S, & Page, L. 1998. The anatomy of a large-scale hypertextual web search engine. Computer Network and ISDN Systems, 30, 107-117.

  • Labbé, D., & Monière, D. 2003. Le discours gouvernemental. Canada, Québec, France (1945-2000). Honoré Champion: Paris.

  • Labbé, D., & Monière, D. 2008. Les mots qui nous gouvernent. Le discours des premiers ministres québécois: 1960-2005. Monière-Wollank: Montreal.

  • Labbé, D., & Savoy, J. 2021. Stylistic analysis of the French presidential speeches: Is Macron really different? Digital Scholarship in the Humanities, to appear.

  • Pauli, F., & Tuzzi, A. 2009. The end of year addresses of the presidents of the Italian republic (1948–2006): Discourse Similarities and Differences. Glottometrics, 18, 40–51.

  • Savoy, J. 2015. Vocabulary growth study: An example with the State of the Union Addresses. Journal of Quantitative Linguistics, 22, 289-310.

  • Savoy, J. 2015. Text clustering: An application with the State of the Union addresses. Journal of the American Society for Information Science and Technology, 66, 1645-1654.

  • Kubát, M., & Cech, R. 2016. Quantitative analysis of US presidential inaugural addresses. Glottometrics, 34, 14-27.

  • Sylwester, K., & Purver, M. 2015. Twitter language use to reflects psychological differences between Democrats and Republicans. PLoS One, 10(9).

  • Raubach, E.E. 2019. Does political ideology influence how politicians speak? A linguistic analysis of one-minute speeches from the US House of Representatives of the 115th Congress. Document numérique, 22(1-2), 127–138.

  • Yu, B. 2008. Classifying party affiliation from political speech. Journal of Information Technology and Politics, 5, 33-48.

  • Yu, B. 2013. Language and gender in congressional speech. Literary and Linguistic Computing, 29,118-132.

  • Pennebaker, J. 2011. The secret life of pronouns. What our words say about us. Bloomsbury Press: New York (NY).

  • O’Connor, B., Balasubramanyan, R., Routledge, B., & Smith, N. 2010. From tweets to polls: Linking text sentiment to public opinion time series. Proceedings 4th International AAAI Conference on Weblogs and Social Media, 122-129.

  • Wilson, T., Hoffmann, P., Somasundaran, S., Kessler, J., Wiebe, J., Choi Y., Cardie, C., Riloff, E., & Patwardhan, S. 2005. OpinionFinder: A system for subjectivity snalysis. Proceedings Empirical Methods for Natural Language Processing, Vancouver (BC), 34-35.

  • Mohammad, S.M., Zhu, X., Kiritchenko, S., & Martin, J. 2015. Sentiment, emotion, purpose, and style in electoral tweets. Information Processing & Management, 51, 480-499.

  • Arnold, E., & Labbé, D. 2015. Vote for me. Don’t vote for the other one. Journal of World Languages, 2, 32-49.

  • Young, L., & Soroka, S. 2012. Affective news: The automated coding of sentiment in political texts. Political Communication, 29, 205-231.

  • Hart, R. 1984. Verbal style and the presidency. A computer-based analysis. Academic Press: Orlando (FL).

  • Bligh M., Merolla J., Schroedel J., & Gonzalez, R. 2010. Finding her voice: Hillary Clinton rhetoric in the 2008 presidential campaign. Woman’s Studies, 39, 823-850.

  • Hart, R. 2020. Trump and us: What he Says and Why People Listen. Cambridge University Press: Cambridge (UK).

  • Tausczik, Y., & Pennebaker, J. 2010. The psychological meaning of words: LIWC and computerized text analysis methods. Journal of Language and Social Psychology, 29, 24-54.

  • Slatcher, R., Chung, C, Pennebaker, J., & Stone, L. 2007. Winning words: Individual differences in linguistic style among U.S. presidential and vice presidential candidates. Journal of Research in Personality, 41, 63-75.

  • Lichtman, A. 2016. Predicting 2016: How the 13 keys to the White House will turn in 2016. HubPages, June 7th. URL: hubpages.com/politics/Predicting-the-2016-Election-the-13-Keys-to-the-White-House.

  • Granka, L. 2013. Using online search traffic to predict US presidential elections. Political Science and Politics, 46, 271–279.

  • Hargittai, E. 2015. Is bigger always better? Potential biases of big data derived from social network sites. Annals of the American Academy of Political and Social Science, 659, 63-76.

  • Boller, P. Jr. 2004. Presidential campaigns. From George Washington to George W. Bush. Oxford University Press: Oxford.

  • Eisenstein, F. J. 2019. Introduction to Natural Language Processing. The MIT Press: Cambridge (MA).

  • Glauser, C., Schmid, L., Kurpicz-Briki, M. 2020. Identifying the public interest in COVID-19 contact tracing apps in Switzerland based on online search behaviour. In: Proceedings of the 13th International Conference on Theory and Practice of Electronic Governance (ICEGOV2020). The ACM Press: New York.

  • Cleverdon, C., Mills, J, & Keen, M. 1966. Factors determining the performance of indexing systems. Technical Report, Cranfield (UK), ASLIB Cranfield Research Project.

  • Ehrenfreud, M., & Tankersley, J. 2016. Seven things to know about Hillary Clinton and Donald Trump before you vote. Washington Post, November 7th.

  • Furnas, G., Landauer, T., Gomez, L., & Dumais, S. 1987. The vocabulary problem in human-system communication. Communications of the ACM, 30, 964-971.

  • Manning, C., Raghavan, P., & Schütze, H. 2008. Introduction to Information Retrieval. Cambridge University Press: Cambridge (MA).

  • Hart, R., Childers, J, & Lind, C. 2013. Political Tone. How Leaders Talk and Why. The University of Chicago Press: Chicago (IL).

  • Hart, R.P., & Curry, A.L. 2016. The third voice of America politics. Presidential Studies, 46(1), 73-97.

  • Grimm, L.G. 1993. Statistical Applications for the Behavioral Sciences. John Wiley & Sons: New York.

  • Lakoff, G., & Wehling, E. (2012). The Little Blue Book: The Essential Guide to Thinking and Talking Democratic. Free Press, New York.

  • Carpenter, R.H., & Seltzer, R.V. (1970). On Nixon's Kennedy Style. Speaker and Gavel, 7, 41-43.

  • Marcus, G., MacKuen M.B. 1993. Anxiety, enthusiasm, and the vote: The emotional underpinning of learning and involvement during presidential campaigns. American Political Science Review, 87(3), 672-685.

  • Ansolabehere S, & Iyengar S. 1995. Going Negative: How Attack Ads Shrink and Polarize the Electorate. New York: Free Press.

  • Brader, T. 2005. Strinking a responsive chord: How political ads motivate and persuade voters by appealing to emotions. American Journal of Political Science 49(2), 388-405.

  • Sides, J., Tesler, M., & Vavreck, L. 2018. Identity crisis: The 2016 presidential campaign and the battle for the meaning of America. Princeton University Press: Princeton.

  • Glauser C., Savoy J., Schmid L. 2021. User searches on multiple channels during the US presidential election 2020. Journal of Political Sciences & Public Affairs, 9(3), Article #3.


Abstract Views: 231

PDF Views: 125




  • Monitoring of The Last Us Presidential Elections

Abstract Views: 231  |  PDF Views: 125

Authors

Christoph Glauser
Department of IFAAR Institute, Bern, Switzerland
Jacques Savoy
Computer Science Dept., University of Neuchatel, Switzerland
Loris Schmid
Department of IFAAR Institute, Bern, Switzerland

Abstract


This paper presents the results of a new monitoring project of the US presidential elections with the aim of establishing computer-based tools to track in real time the popularity or awareness of candidates. The designed and developed innovative methods allow us to extract the frequency of queries sent to numerous search engines by US Internet users. Based on these data, this paper demonstrates that Trump was more frequently searched than the Democratic candidates, either Hillary Clinton in 2016 or Joe Biden in 2020. When analyzing the topics, it is observed that in 2020 the US users had shown a remarkable interest in two subjects, namely, Coronavirus and Jobs (unemployment). Interest for other topics such as Education or Healthcare were less pronounced while issues such as Immigration were given even less attention by users. Finally, some “flame” topics such as Black Lives Matter (2020) and Gun Control (2016) appear to be very popular for a few weeks before returning to a low level of interest. When analyzing tweets sent by candidates during the 2020 campaign, one can observe that Trump was focused mainly on Jobs and on Riots, announcing what would happen if Democrats took power. To these negative ads, Biden answered by putting forward moral values (e.g., love, honesty) and political symbols (e.g., democracy, rights) and by underlying the failure of the current administration in resolving the pandemic situation.

Keywords


E-Government, Online Searches, Search Engines, Discourse Analysis, Political Science.

References