مطالعات فرهنگی و ارتباطات

نوع مقاله : مقاله پژوهشی

نویسندگان

1 استادیار علوم ارتباطات، دانشکده مطالعات جهان، دانشگاه تهران، تهران، ایران

2 دانشیار گروه ارتباطات دانشکده‌ علوم اجتماعی دانشگاه تهران:

3 دانشجوی دکتری / دانشگاه تهران

چکیده

در عصر شبکه‌های اجتماعی بسیاری از جنبه‌های زندگی افراد در ارتباط با پیام تعریف می‌شود. بسیاری از اعضای این شبکه‌ها موفقیت خود را در میزان لایک و به اشتراک گذاری پیامهایشان می‌بینند، شرکت‌ها هم در تلاش هستند تا در یابند مشتریانشان چه پیام‌هایی را می‌پسندند و چه پیام‌هایی را به اشتراک می‌گذارند. مطالعاتی که تا کنون انجام شده سعی داشته‌اند تا با استفاده از ویژگی‌های شخصیتی یا جمعیت شناختی کاربران به پیش‌بینی‌هایی در زمینه‌ی واکنش افراد به پیام‌های مختلف دست بزنند. اما اکنون تلاش‌هایی در حال انجام است تا این نوع مطالعات دقیق‌تر شوند و قدرت پیش‌بینی بیشتری داشته باشند. این مطالعه با یک رویکرد خلاق شبه آزمایشگاهی تلاش کرد تا میان باورهای افراد و رفتاری‌هایی که در شبکه‌های اجتماعی در قبال پیام‌ها انجام می‌دهند، روابط معنی‌داری پیدا کند. یافته‌های این مطالعه نشان دادند که میزان آشنایی با باورهای افراد و همچنین تفکیک پیام‌های شبکه‌های اجتماعی به ایده‌های محوری که در آنها نهفته است، میتواند روش عملی‌ای برای پیش‌بینی این امر باشد که چه افرادی چه پیام‌هایی را لایک می‌کنند.

کلیدواژه‌ها

موضوعات

INTRODUCTION

Until a few decades ago, during the era of mass media, important players of the media environment were the major media outlets that sought to capture more of people's attention, eyes, and minds through their printed or broadcasted materials. They aimed to persuade people to show certain political behaviors or purchase particular products (Khanlari & Zamanian, 2013; Rahimnia, Ramezani, & Zargaran, 2018). However, today, with the emergence of social media, the realm of communication has become a playground for billions of individuals who spend a significant portion of their lives engaging in receiving, sending, sharing, reposting, and sometimes creating messages. The success of the users on these networks is usually evaluated by factors such as the number of their followers and the number of likes and shares their materials get. This study is an effort to provide an explanation for the differing longevity and impact of different social media messages.

Every day millions of social media messages compete for attention from the users and survival and dissemination on the network. Not only public relations agents, advertising, and marketing departments, but also individuals who create and share content on social media compete to get more likes and shares on a daily basis. The present study is the result of an attempt to better understand the factors that make a message more likable, shareable, and durable on social media and employs a quasi-experimental method.

This study assumes that beliefs are the determining factor in individuals' relationship with messages including social media messages. The study’s hypothesis is that a practical way to explain the different faiths of social media messages is to examine the relationships between the beliefs held by the members of social media (nodes of the network) and their reactions to social media messages. This is to say, if we have a comprehensive understanding of people’s beliefs we may be able to practically explain and even predict their behaviors on social media – namely by predicting the messages they would like, share, and so on.

METHODOLOGY 

The project was conducted in two steps. In the first step, a thematic analysis of the messages posted on popular Instagram pages was conducted. Through this, several recurring ideas were extracted. Consequently, two specific beliefs were chosen from the pool of extracted ideas: ‘Iran is a significant military power' and 'Prayers help countries progress'. The second step was a quantitative survey. Here, the researchers designed a two-part questionnaire. In the first part, the level of adherence of individuals to each of the two chosen beliefs was measured using the Likert scale.  The second part of the questionnaire included several messages that were related to the two beliefs. The participants were asked to indicate their responses to the messages through likes, comments, and shares. The questionnaire was completed by 393 residents of Tehran.

After the data collection was completed, the questionnaire data was analyzed using the statistical analysis application, SPSS, in order to find any significant relationships between the participants’ beliefs and their reactions to the messages. The goal of the analysis was mainly to check if such a method would be practical to explain or predict people’s reactions if we have an understanding of their beliefs. The analysis resulted in several significant relationships which indicates that the proposed method could be practical and useful.

FINDINGS 

When a message contained an idea related to belief A, the people who believed in A were more likely to like the message. The same was observed with belief B. Interestingly when a message contained ideas related to the two beliefs, the people who were the most likely to like those messages were the people who both believed in A and B. The findings showed that it is practical to survey people’s ideas and beliefs and then explain their reactions to messages on social media.

Clearly, this is only preliminary work on a small sample of ideas. In order to be able to predict people’s reactions to a wide range of messages online, one would gain a detailed understanding of their ideas and beliefs on many different topics. Also, more complicated mathematical analysis would be required to gain high predicting power when it comes to incorporating our wide knowledge about people’s beliefs into a practical way to understand their reactions to different messages.

It should be mentioned that some of the relationships observed in this study indicate weak relationships between beliefs and the desired reactions, the fact that there is a relationship, and in most cases, these relationships are in the medium to strong range, gives hope for further studies and more accurate predictions. It is important to keep in mind that the goal of this study is not to manipulate individuals' beliefs and attitudes to a specific political or economic behavior or action in social media networks. A much more valuable and different interpretation of these types of studies is that as actors, policymakers, or even ordinary individuals, in order to be able to communicate with people in the age of social media and be seen and heard and face less neglect or negative reactions from people, we need to know their mindsets, beliefs, and concerns.

This study revealed that messages inherently carry beliefs, and by identifying individuals' adherence to these beliefs and assessing their behavior in response to belief-laden messages, the destiny of a message can be predicted to some extent. Furthermore, gaining insights into people's online behavior through this approach holds crucial implications for politicians and activists: The key to truly understanding people's actions on social media lies in comprehending their beliefs, concerns, pains, problems, fears, and hopes. Scientific research offers an effective and reliable means of understanding people's perspectives, and beliefs, and how these factors influence their responses to messages within the realm of social media.

حسین پور، جعفر؛ اسدی فرد، محمد؛ براری حسین. (1397)، رابطه بین شبکه پیام‌رسان تلگرام با ایجاد ناآرامی های قومی در مناطق کردنشین استان کرمانشاه از نگاه کارشناسان پلیس امنیت. پژوهشنامه مطالعات مرزی، 103-125.
خانلری، ا. و زمانیان، ص. (1393)، بررسی رابطه بین وفاداری به برند و تبلیغات توصیه‌ای الکترونیک در شبکه‌های اجتماعی. دوفصلنامه کاوش های مدیریت بازرگانی، 75-99.
رحیم‌نیا, ف.، رمضانی, ی.، و زرگران، س. (1398). تأثیر تعامل کاربران در رسانه‌های اجتماعی بر قصد خرید به واسطه نگرش نسبت به برند و هنجارهای ذهنی. فصلنامه علمی تحقیقات بازاریابی نوین، 33-52.
فرهنگی، علی اکبر؛ دیگران (1393). تحلیل تاثیر رسانه های اجتماعی بر نگرش مشتریان نسبت به نام تجاری و قصد خرید از شرکت: مطالعه موردی شرکت ایران خودرو. جهانی رسانه، 236-251.
عبداللهی‌نژاد، ع؛ و صبار، ش. (1399) دین‌داران و رفتار شبکه‌ای: دینداران چگونه در فضای مجازی عمل می‌کنند. تحلیل مصرف رسانه‌ای. پژوهشگاه فرهنگ هنر و ارتباطات. 179-200.
علیزاده‌زوارم، ع؛ رجب‌زاده، محمدرضا. (1396) بررسی عوامل موثر بر رفتار خرید اینترنتی مشتریان با استفاده نظریه برنامه برنامه‌ریزی شده. رویکردهای پژوهشی نوین در مدیریت و حسابداری، 13-32
کوهزادی، فواد؛ قره بیگلو، حسین؛ بوداقی خواجه نوبر، حسین؛ علوی متین، یعقوب. (1401)  طراحی مدل تجزیه و تحلیل رفتار مشتریان مبتنی بر کلان داده با استفاده از روش فرا ترکیب و دلفی. مطالعات رفتار مصرف‌کننده، 32-54.
 Abdollahinezhad, A.; & Sabbar, S. (2019). Religious people and network behavior: How religious people act in cyberspace. In K. Azizi-Mehr. Analysis of media consumption (pp. 179-200). Institute of Culture, Art and Communication: Tehran. (In Persian).
Ahmed, S.; & Madrid-Morales, D. (2020). Is it still a man’s world? Social media news use and gender inequality in online political engagement. Information, Communication & Society, 381-399.
Alhabash, S.; & R McAlister, A. (2015). Redefining virality in less broad strokes: Predicting viral behavioral intentions from motivations and uses of Facebook and Twitter. New media & society, 1-23.
Alizadeh-Zwarom, A. & Rajabzade, M. (2016). The factors affecting the online shopping behavior of customers using the theory of the planned program. New research approaches in management and accounting, 32-13. (In Persian).
Baileya, A.; & et al. (2021). Modeling consumer engagement on social networking sites: Roles of attitudinal and motivational factors. Journal of Retailing and Consumer Services. https://doi.org/10.1016/j.jretconser.2020.102348
Beauchamp, C. (2010). Who invented the telephone: Lawyers, patents, and the judgments of history. Technology and Culture, 51(4). https://doi.org/10.1353/tech.2010.0038
Darr, J.; & et al. (2018). Newspaper Closures Polarize Voting Behavior. Journal of Communication, 1007–1028.
Del Vicario, M.; & et al. (2019). Polarization and Fake News: Early Warning of Potential Misinformation Targets. ACM Transactions on the Web, 13(2), 1-22. doi:https://doi.org/10.1145/3316809
Del Vicario, M.; & et al. (2016). The spreading of misinformation online. The National Academy of Sciences, 1-6.
Dhir, A.; & et al. (2018). Rationale for “Liking” on Social Networking Sites. Social Science Computer Review, 37(4), 529-550. https://doi.org/10.1177/0894439318779145
Driss, O.; & et al. (2019). From citizens to government policy-makers: Social media data analysis. Government Information Quarterly, 36(3), 560-570.
Farhangi, A. et.al. (2013). Analyzing the impact of social media on consumer attitudes toward the brand and their intention to purchase. Global Media, 236-251. (In Persian).
Fazio, R. (1990). Multiple Processes by Which Attitudes Guide Behavior: The Mode Model as an Integrative Framework. In M. P. Zanna, Advances in Experimental Social Psychology, Vol. 23, pp. 75-109. Siin Diego. Calitornia: ACADEMIC PRESS. INC.
Fishbein, M.; & Ajzen, I. (1975). Belief, Attitude, Intention, and Behavior: An Introduction to Theory and Research. London & Amherst: Addison-Wesley Publishing Company.
França, R.P.; Monteiro, A.C.B.; Arthur, R.; Iano, Y. (2021). An Overview of the Edge Computing in the Modern Digital Age. In: Chang, W.; Wu, J. (eds) Fog/Edge Computing for Security, Privacy, and Applications. Advances in Information Security, vol 83. Springer, Cham. https://doi.org/10.1007/978-3-030-57328-7_2
Gurmukhani, N. (2021, Jun 10). Social Media Algorithms: Everything You Need to Know. Retrieved from https://www.upgrad.com/blog/how-do-social-media-algorithm-works/
Hosseinpour, J.; AsadiFard, M.; Barari, H. (2017). The Relationship Between Telegram Messenger Network with Ethnic Unrest in Kurdish Regions Of Kermanshah Province From Security Police Expert. Journal of Frontier Studies, 103-125. (In Persian).
Jami Pour, M.; & Taheri, F. (2019). Personality traits and knowledge sharing behavior in social media: mediating role of trust and subjective well-being. On the Horizon, 98-117.
Karnowski, V.; & et al. (2018). Why Users Share the News: A Theory of Reasoned Action-Based Study on the Antecedents of News-Sharing Behavior. Communication Research Reports, 91-100.
Karatsoli, M.; & Nathanail, E. (2020). Examining gender differences of social media use for activity planning and travel choices. European Transport Research Review. doi: 10.1186/s12544-020-00436-4
Kaufhold, M.; Rupp, N.; Reuter C.; Habdank M. (2020) Mitigating information overload in social media during conflicts and crises: design and evaluation of a cross-platform alerting system, Behaviour & Information Technology, 39:3, 319-342, DOI: 10.1080/0144929X.2019.1620334
Khanlari, A. & Zamanian, S. (2013). The relationship between brand loyalty and advertising Electronic Word of Mouth Marketing on Social media. Bi-Quarterly Journal of Business Management Explorations, 75-99. (In Persian)
Kouhzadi, F. et al. (2022) Designing a Model for Analyzing Customer Behavior on Big Data Using Meta-Synthesis Method and Delphi Method. Studies in Consumer Behavior, 32-54. (In Persian)
Lee-Won, R. J.; & et.al. (2017). The Effects of Social Media Virality Metrics, Message Framing, and Perceived Susceptibility on Cancer Screening Intention: The Mediating Role of Fear. Telematics and Informatics, 1-34.
McLaughlin, C.; & Stephens, S. (2015). The theory of planned behavior: the social media intentions of SMEs. Irish Academy of Management (pp. 1-31). NUIG.
Oliveira, J.; & et al. (2022). The effect of emotional positivity of brand-generated social media messages on consumer attention and information sharing. Journal of Business Research, 49-61.
Ozanne, M.; & et al. (2017). An Investigation into Facebook “Liking” Behavior An Exploratory Study. Social Media + Society, 3(2), 1-12. doi: 10.1177/2056305117706785
Rahimnia, F.; Ramezani, Y.; & Zargaran, S. (2018). The Effect of Users' Interaction in Social Media on Purchasing by Brand Attitude and Subjective Norms. Scientific Quarterly of Modern Marketing Research, 33-52. (In Persian)
Roux, I. L.; & Maree, T. (2016). Motivation, engagement, attitudes and buying intent of female Facebook users. Acta Commercii - Independent Research Journal in the Management Sciences, 1-11. DOI: 10.4102/ac. v16i1.340
Sabbar, S.; & Hyun, D. (2016). What makes it likeable? A study on the reactions to messages in a digital social network: the case of Facebook in Farsi. SpringerPlus. https://doi.org/10.1186/s40064-016-3771-3
Sabzali, M.; Sarfi, M.; Zohouri, M.; Sarfi, T.; Darvishi, M. (2022). Fake News and Freedom of Expression: An Iranian Perspective. Journal of Cyberspace Studies, 6 (2), 205-218. doi: 10.22059/JCSS.2023.356295.1087
Swani, K.; & Labrecque, L. I. (2020). Like, Comment, or Share? Self-presentation vs. brand relationships as drivers of social media engagement choices. Marketing Letters, 31(2–3). https://doi.org/10.1007/s11002-020-09518-8
Taylor, R. (2020). “Data localization”: The internet in the balance. Telecommunications Policy, 44(8). doi:10.1016/j.telpol.2020.102003.
Tellis, G.; & et al. (2019). What Drives Virality (Sharing) of Online Digital Content? The Critical Role of Information, Emotion, and Brand Prominence. American Marketing Association, 1-20.
Zhao, T. (2018). Analysis of the Concept of Audience in the Digital Age. International Workshop on Education Reform and Social Sciences (pp. 132-137). London: atlantis-press.