شناسایی و ارزیابی الگوهای تأثیرپذیری کاربران ایرانی توییتر از نگرش‌های سیاسی اینفلوئنسرها

نوع مقاله: مقاله پژوهشی

نویسندگان

1 دانشیار گروه علوم اجتماعی دانشگاه تهران

2 دانشجوی دکتری جامعه‌شناسی اقتصادی و توسعۀ دانشگاه تهران

چکیده

در این پژوهش، اجزای تشکیل‌دهندة «توییتر ایرانی» و الگوهای تأثیرپذیری کاربران ایرانی توییتر از نگرش‌های سیاسی اینفلوئنسرهای توییتر ایرانی را شناسایی و ارزیابی کرده‌ایم. بدین‌منظور اینفلوئنسرها و دنبال‌کننده‌ها (فالوئرها) براساس گرایش‌های سیاسی‌شان دسته‌بندی شده‌اند و الگوهای تأثیرپذیری دنبال‌کننده‌ها از اینفلوئنسرها به تفکیک گرایش‌های مختلف، شناسایی و ارزیابی شده‌ است. در دسته‌بندی اینفلوئنسرها و دنبال‌کننده‌ها از تکنیک‌های داده‌کاوی و در تحلیل داده‌های شبکه‌ای، از تحلیل‌های نرم‌افزار گفی (Gephi) استفاده کرده‌ایم. نتایج این پژوهش نشان می‌دهد، با وجود آنکه هیچ‌کدام از گرایش‌های سیاسی در توییتر ایرانی، از ‌لحاظ مقبولیت در میان کاربران و اینفلوئنسرها، برتری محسوسی به گرایش‌های رقیب ندارند، بخش قابل‌توجهی از کاربرانی که طرفدار هریک از گرایش‌های سیاسی موجودند، معمولاً تمایل کمی دارند که در معرض پیام‌های سیاسی متفاوت قرار بگیرند و بیشتر سعی می‌کنند مطالبی منطبق یا نزدیک به نگرش‌های سیاسی خودشان را بخوانند.

کلیدواژه‌ها


عنوان مقاله [English]

Exploring and Evaluating the Patterns Iranian Twitter Users Are Affected by the Political Attitudes of Influencers

نویسندگان [English]

  • Hamidreza Jalaeipour 1
  • Abolfazl Hajizadegan 2
1 Department of Sociology, Faculty of Social Sciences, University of Tehran, Tehran, Iran
2 PhD Student, Sociology of Development, Faculty of Social Sciences, University of Tehran, Tehran, Iran
چکیده [English]

Introduction: In this research, through identifying the components of what is called “Iranian Twitter”, we tried to identify and evaluate the patterns Iranian Twitter users are affected by the political attitudes of the Iranian Twitter Influencers.
Method: In this study we decided to analyze the data on the communication networks between users and Influencers. The steps we took to answer the three questions of research were as follows: We identified the Iranian Twitter Influencers using snowball sampling. Then, by reviewing the pages of the influencers one by one, we recorded their characteristics in our list. Then, we collected the information about the communication network between the influencers, and used that as input for Gephi software. Finally, we calculated each group of users attributed to each political attitude, how much expose themselves to influencers with different political attitudes, on average.
Results and discussion: two-fifths of Principality users are considered to be fanatic users. A fanatic user is a person who follows more than 70% of the influencers  and has the same political attitude. In fact, the fanatic user is one who is often exposed to one type of political attitude and is less inclined to be exposed to different opinions. Only 1% of transformationalist users were fanatic users. On average, 56% of the influencers followed by a reformist user with reformist attitudes. Also, transformationalist users are more likely to follow non-political influencers (44%) than to follow transformationalist influencers (33%). The complete isolation of separatists is evident in this table, as they have no share in the influencers followed by reformist, transformationalist, and principalist users, and only 1% of the them are followed by subversives supported the separation of Iran. Further, 64% of  influencers are also followed by principalist users tended to be principalist.
Conclusion: The results showed that although none of the political attitudes in Iranian Twitter have significant superiority to others, a significant proportion of users who favored a political attitude, displayed little tendency to be exposed to different political messages. Another important point is that online social networks should not necessarily be seen as a tool for organizing movements or revolutions. Influencers of online social networks affect attitudes of ordinary users by the contents that they publish constantly in long term. The continuous and daily influence of these new media in shaping the lifestyle, habits and attitudes of users is also important. If we do not understand the important role the online social networks could play in daily lives of users, we could not understand how they influence in political contexts.

کلیدواژه‌ها [English]

  • online social networks
  • Twitter
  • Influencer
  • Political Attitudes
  • کاستلز، مانوئل، (1396)، شبکه‌های خشم و امید: جنبش‌های اجتماعی در عصر اینترنت، ترجمة مجتبی قلی‌پور، تهران: نشر مرکز.
    • Barberá, P., Jost, J., Nagler, J., Tucker, J., and Bonneau, R. (2015), “Tweeting From Left to Right: Is Online Political Communication More Than an Echo Chamber?”, Psychological Science, No. 26, 1-12.
    • Bauman, Z. (2016), “Zygmunt Bauman: Social Media Are a Trap: The Polish-born Sociologist Is Skeptical About The Possibilities for Political Change”. Retrieved from: https://english.elpais.com/elpais/2016/01/19/inenglish/1453208692_424660.html .
    • Blockspring. (2018), Retrieved from Blockspring: https://www.blockspring.com/
    • Colleoni, E., Rozza, A., and Arvidsson, A. (2014), “Echo Chamber or Public Sphere? Predicting Political Orientation and Measuring Political Homophily in Twitter Using Big Data”, Journal of Communication, No. 64, 317-332.
    • Dubois, E., and Blank, G. (2018), “The Echo Chamber Is Overstated: The Moderating Effect of Political Interest and Diverse Media”, Information, Communication & Society, No. 21, 729-745.
    • Faina, J. (2012), “Twitter and the New Publicity”, Institute of General Semantics, No. 69, 55-71.
    • Gephi. (2018), about, retrieved from gephi: https://gephi.org/about/
    • Heymann, S. (2015), Page Rank, Retrieved from:
https://github.com/gephi/gephi/wiki/PageRank
  • Mertia, S. (2014), Theorising Identity on Twitter, Retrieved from
https://www.academia.edu/5410689/Theorising_Identity_on_Twitter
  • Morris, T. (2009), All a Twitter: A Personal and Professional Guide to Social Networking with Twitter, Indianapolis: Que.
  • Nodus Labs. (2018), Network Visualization and Analysis with Gephi, Retrieved from: https://noduslabs.com/courses/network-visualization-and-analysis-with-gephi/
  • Omincore. (2018), Twitter by the Numbers: Stats, Demographics and Fun Facts, Retrieved from: https://www.omnicoreagency.com/twitter-statistics/
  • Pérez-Altable, L. (2016), The Arab Spring before the Arab Spring: A Case Study of Digital Activism in Tunisia, Retrieved from:
https://www.researchgate.net/publication/294548657_The_Arab_Spring_before_the_Arab_Spring_A_case_study_of_digital_activism_in_Tunisia
  • Statcounter. (2018), Social Media Stats Islamic Republic of Iran, Retrieved from: http://gs.statcounter.com/social-media-stats/all/iran