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Journal articles on the topic 'Social influence'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 October 2024

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1

Smith, Brian G., Megan C. Kendall, Devin Knighton, and Temi Wright. "Rise of the Brand Ambassador: Social Stake, Corporate Social Responsibility and Influence among the Social Media Influencers." Communication Management Review 03, no. 01 (July 30, 2018): 6–29. http://dx.doi.org/10.22522/cmr20180127.

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2

Ju, Weijia, and Ling Chen. "Influence Maximization in Social Network with Negative Influence." International Journal of Machine Learning and Computing 9, no. 2 (April 2019): 230–35. http://dx.doi.org/10.18178/ijmlc.2019.9.2.791.

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3

McDonald, Rachel I., and Christian S. Crandall. "Social norms and social influence." Current Opinion in Behavioral Sciences 3 (June 2015): 147–51. http://dx.doi.org/10.1016/j.cobeha.2015.04.006.

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4

Assensoh-Kodua, Akwesi. "Social purchasing and the influence of social networking: a conceptual view." Banks and Bank Systems 11, no. 3 (October 12, 2016): 44–57. http://dx.doi.org/10.21511/bbs.11(3).2016.05.

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Internet has enabled businesses to offer their merchandise through web-based applications, of which recent phenomenon includes online social networks (OSNs). This paper studies the influence of OSNs through the lens of perceived trust (PT), social norm (SN), user satisfaction (US) and perceived behavioral control (PBC) to find out how these influence participants of OSNs continuance buying intention. A model of IS continuance intention of web-based application was developed to test the above factors. The results show that trust in OSN is based mainly on the degree of the social relations that users have with their vendors, because they are members on the network, on top of their experiences of web service use. US was influenced by PBC, while US also influenced SN and PT with PT exhibiting a strong relationship with SN. Keywords: continuance intention, OSN, participants, purchasing. JEL Classification: Z13, G21, M10, M31, D11, D12

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5

Dr R Venkatapathy, Dr R. Venkatapathy, and Valarrmathi V. Valarrmathi V. "Innovative Trends in Corporate Social Responsibility and Influence of Social Media." Global Journal For Research Analysis 3, no. 4 (June 15, 2012): 234–35. http://dx.doi.org/10.15373/22778160/apr2014/81.

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6

Okdie, Bradley M., Rosanna E. Guadagno, Petia K. Petrova, and Wyley B. Shreves. "Social Influence Online." International Journal of Interactive Communication Systems and Technologies 3, no. 1 (January 2013): 20–31. http://dx.doi.org/10.4018/ijicst.2013010102.

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This study examined the extent to which communicator salience manipulated by varying communication modes, authority-based social influence, and gender affect persuasion in online environments by utilizing a 2 by 2 between subjects design. Participants of the experiment were either presented with an authority-based influence attempt or no influence attempt. They then engaged in a persuasive interaction with a same-sex confederate via computer-mediated communication (CMC) or face-to-face. Results revealed that men in the Authority condition who interacted via CMC were more persuaded then men in the Peer condition who interacted via CMC. Additionally, men reported more confidence when interacting via CMC and reported that their decision was more influenced by the confederate online. Moreover, perceptions of the confederate varied by gender and communication mode. Analysis suggests that authority based influence tactics via CMC are more effective for men than for women.

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7

Dallas, Rebecca, Matt Field, Andrew Jones, Paul Christiansen, Abi Rose, and Eric Robinson. "Influenced but Unaware: Social Influence on Alcohol Drinking Among Social Acquaintances." Alcoholism: Clinical and Experimental Research 38, no. 5 (March 3, 2014): 1448–53. http://dx.doi.org/10.1111/acer.12375.

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8

Gomes, Adriano José da Silva, Juliana Francisca Cecato, José Montiel, José Eduardo Martinelli, and Gleiber Couto Santos. "Influence of Social Skills in educational processes." Perspectivas Médicas 25, no. 1 (April 1, 2014): 20–26. http://dx.doi.org/10.6006/perspectmed.20140103.5720327683.

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9

ANDRONIC, Răzvan-Lucian, and Anca-Olga ANDRONIC. "EVALUATION AND SOCIAL INFLUENCE IN SCHOOL EDUCATION." SCIENTIFIC RESEARCH AND EDUCATION IN THE AIR FORCE 18, no. 2 (June 24, 2016): 505–8. http://dx.doi.org/10.19062/2247-3173.2016.18.2.2.

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10

Offermann, Lynn R., and Pamela E. Schrier. "Social Influence Strategies." Personality and Social Psychology Bulletin 11, no. 3 (September 1985): 286–300. http://dx.doi.org/10.1177/0146167285113005.

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11

Mandel, David R. "Military Social Influence." Analyses of Social Issues and Public Policy 11, no. 1 (February 9, 2011): 39–42. http://dx.doi.org/10.1111/j.1530-2415.2011.01229.x.

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12

Dhami, Mandeep K. "Military Social Influence." Analyses of Social Issues and Public Policy 11, no. 1 (March 29, 2011): 31–34. http://dx.doi.org/10.1111/j.1530-2415.2011.01239.x.

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13

Smorthit, Kelly, and Jessica Cooper. "Social media influence." BDJ In Practice 33, no. 6 (June 2020): 5. http://dx.doi.org/10.1038/s41404-020-0427-3.

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14

Förster, Manuel, Michel Grabisch, and Agnieszka Rusinowska. "Anonymous social influence." Games and Economic Behavior 82 (November 2013): 621–35. http://dx.doi.org/10.1016/j.geb.2013.09.006.

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15

KONOPLIANNYKOVA, Marianna. "Influence marketing in social media." Scientia fructuosa 154, no. 2 (April 11, 2024): 118–44. http://dx.doi.org/10.31617/1.2024(154)07.

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This article aims to comprehensively explore the evolving landscape of influence marketing, specifically focusing on its impact on brand perception and consumer engagement. Delving into influencer characteristics, the evolution of marketing strategies, and successful campaigns, the study seeks to illuminate the multifaceted dynamics that contribute to the effectiveness of influence marketing in the digital era. The analysis reveals that influence marketing serves as a cornerstone for building authentic connections in an era marked by digital saturation. Insights into emerging trends, such as the rise of nano-influencers and purpose-driven collaborations, underscore the pivotal role influencers play in shaping brand narratives. Innovations like virtual reality integration further highlight the dynamic nature of influence marketing. The emphasis on long-form storytelling, sustainability, and genuine engagement reinforces the enduring importance of influencers as cultural mediators and trusted conduits between brands and consumers. The scientific novelty lies in a nuanced exploration of influence marketing, transcending traditional promotional strategies. The study delves into the psychological impact of influencer endorsements, the effectiveness of specific content formats, and ethical considerations surrounding virtual influencers. By addressing these dimensions, the research contributes to a deeper understanding of influence marketing dynamics, providing valuable insights for researchers and industry professionals alike. The practical value of this research is two-fold. Firstly, it offers marketers actionable insights into navigating challenges, adapting to emerging trends, and refining influencer strategies. Secondly, the exploration of ethical considerations and the impact of technologies in influence marketing equips industry professsionals with the knowledge needed to make informed decisions, fostering responsible and effective influencer campaigns. This article encourages continuous inquiry, ensuring that influence marketing remains not only a dynamic force in the digital landscape but also an ethically grounded and impactful strategy for building brand connections

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16

Kahan, Dan M. "Social Influence, Social Meaning, and Deterrence." Virginia Law Review 83, no. 2 (March 1997): 349. http://dx.doi.org/10.2307/1073780.

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17

Smith, William P. "Social Influence and the Social Self." Contemporary Psychology: A Journal of Reviews 34, no. 5 (May 1989): 450–51. http://dx.doi.org/10.1037/028017.

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18

Smith, Eliot R., and Diane M. Mackie. "Influence from representations of others’ responses: social priming meets social influence." Current Opinion in Psychology 12 (December 2016): 22–25. http://dx.doi.org/10.1016/j.copsyc.2016.04.012.

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19

Hamid, Zuliah Abd, Shazali Johari, and Puvaneswaran Kunasekaran. "Exploring Social Media’s Influence on Sustainable Indigenous Tourism." International Journal of Psychosocial Rehabilitation 24, no. 02 (February 12, 2020): 1084–91. http://dx.doi.org/10.37200/ijpr/v24i2/pr200411.

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20

Antelmi, Alessia, Gennaro Cordasco, Carmine Spagnuolo, and Przemysław Szufel. "Social Influence Maximization in Hypergraphs." Entropy 23, no. 7 (June 23, 2021): 796. http://dx.doi.org/10.3390/e23070796.

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This work deals with a generalization of the minimum Target Set Selection (TSS) problem, a key algorithmic question in information diffusion research due to its potential commercial value. Firstly proposed by Kempe et al., the TSS problem is based on a linear threshold diffusion model defined on an input graph with node thresholds, quantifying the hardness to influence each node. The goal is to find the smaller set of items that can influence the whole network according to the diffusion model defined. This study generalizes the TSS problem on networks characterized by many-to-many relationships modeled via hypergraphs. Specifically, we introduce a linear threshold diffusion process on such structures, which evolves as follows. Let H=(V,E) be a hypergraph. At the beginning of the process, the nodes in a given set S⊆V are influenced. Then, at each iteration, (i) the influenced hyperedges set is augmented by all edges having a sufficiently large number of influenced nodes; (ii) consequently, the set of influenced nodes is enlarged by all the nodes having a sufficiently large number of already influenced hyperedges. The process ends when no new nodes can be influenced. Exploiting this diffusion model, we define the minimum Target Set Selection problem on hypergraphs (TSSH). Being the problem NP-hard (as it generalizes the TSS problem), we introduce four heuristics and provide an extensive evaluation on real-world networks.

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21

Rambo, Prof Benson. "ASSESSING THE INFLUENCE OF PRODUCT DEVELOPMENT STRATEGIES ON PERFORMANCE WITHIN KENYA'S SUGAR INDUSTRY." Global Journal of Humanities and Social Sciences 03, no. 02 (February 4, 2024): 01–07. http://dx.doi.org/10.55640/gjhss-social-325.

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This study investigates the influence of product development strategies on performance within Kenya's sugar industry. With the challenges facing the sugar sector in Kenya, including fluctuating market demands, competition, and regulatory pressures, the adoption of effective product development strategies becomes imperative for sustained growth and competitiveness. Through a comprehensive analysis of the relationships between product development initiatives and performance indicators such as market share, profitability, and customer satisfaction, this research aims to provide insights into the effectiveness of product development strategies within the Kenyan sugar industry. Drawing upon empirical data and industry insights, the study employs both quantitative and qualitative methods to explore the impact of product innovation, diversification, and differentiation on performance outcomes. The findings shed light on the critical role of product development strategies in enhancing the competitiveness and sustainability of the sugar industry in Kenya.

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22

Aral, Sinan, and Paramveer S. Dhillon. "Social influence maximization under empirical influence models." Nature Human Behaviour 2, no. 6 (May 21, 2018): 375–82. http://dx.doi.org/10.1038/s41562-018-0346-z.

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23

Ng, Sik Hung. "Social Influence and Social Issues: An Introduction." Journal of Social Issues 55, no. 1 (March 1999): 3–5. http://dx.doi.org/10.1111/0022-4537.00100.

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24

Mucchi-Faina, Angelica, Maria Giuseppina Pacilli, and Stefano Pagliaro. "Minority Influence, Social Change, and Social Stability." Social and Personality Psychology Compass 4, no. 11 (October 20, 2010): 1111–23. http://dx.doi.org/10.1111/j.1751-9004.2010.00314.x.

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25

Mood, Carina. "Social Influence Effects on Social Assistance Recipiency." Acta Sociologica 47, no. 3 (September 2004): 235–51. http://dx.doi.org/10.1177/0001699304046250.

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26

Abrams, Dominic, and Michael A. Hogg. "Social Identification, Self-Categorization and Social Influence." European Review of Social Psychology 1, no. 1 (January 1990): 195–228. http://dx.doi.org/10.1080/14792779108401862.

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27

Bauer, Martin W. "Social Influence by Artefacts." Diogenes 55, no. 1 (February 2008): 68–83. http://dx.doi.org/10.1177/0392192107087918.

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28

Echterhoff, Gerald, and William Hirst. "Social Influence on Memory." Social Psychology 40, no. 3 (January 2009): 106–10. http://dx.doi.org/10.1027/1864-9335.40.3.106.

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29

Carli, Linda L. "Gender and Social Influence." Journal of Social Issues 57, no. 4 (January 2001): 725–41. http://dx.doi.org/10.1111/0022-4537.00238.

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30

Hammond, Ross A. "Social influence and obesity." Current Opinion in Endocrinology, Diabetes and Obesity 17, no. 5 (October 2010): 467–71. http://dx.doi.org/10.1097/med.0b013e32833d4687.

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31

Ruben, Brent D., and Ralph A. Gigliotti. "Leadership as Social Influence." Journal of Leadership & Organizational Studies 23, no. 4 (July 25, 2016): 467–79. http://dx.doi.org/10.1177/1548051816641876.

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32

Melamed, David, Scott V. Savage, and Chris Munn. "Uncertainty and Social Influence." Socius: Sociological Research for a Dynamic World 5 (January 2019): 237802311986697. http://dx.doi.org/10.1177/2378023119866971.

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Much research documents that uncertainty is an important factor in the social influence process. We argue that there are two senses in which uncertainty plays a role. First, task uncertainty is a necessary but variable condition for social influence to occur. Second, uncertainty reduction is a mechanism producing social influence. We discuss how tasks can vary in the level of uncertainty they entail and how this impacts the mechanisms resulting in social influence. In this context, we predict that task uncertainty moderates social influence and that uncertainty reduction mediates it. We experimentally test our predictions in the status-to-social-influence process using standard means of studying social influence. Inconsistent with prior work, we do not find that task uncertainty is a moderator, but we do find evidence that uncertainty reduction functions as a mediator. Further, we find that the mediated effect is contingent on task uncertainty.

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33

Spino, Margie A., Laurie A. Dinnebeil, and William F. McInerney. "Social Power and Influence." Young Exceptional Children 16, no. 4 (August 14, 2013): 17–30. http://dx.doi.org/10.1177/1096250613493191.

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34

Boden, Joseph M. "Social influence and vulnerability." Behavioral and Brain Sciences 31, no. 4 (July 29, 2008): 442–43. http://dx.doi.org/10.1017/s0140525x08004780.

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AbstractRedish et al. outline 10 vulnerabilities in the decision-making system that increase the risks of addiction. In this commentary I examine the potential role of social influence in exploiting at least one of these vulnerabilities, and argue that the needs satisfied by social interaction may play a role in decision-making with regard to substance use, increasing the risks of addiction.

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35

Molinero, Xavier, Fabián Riquelme, and Maria Serna. "Cooperation through social influence." European Journal of Operational Research 242, no. 3 (May 2015): 960–74. http://dx.doi.org/10.1016/j.ejor.2014.11.006.

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36

Fershtman, Chaim, and Uzi Segal. "Preferences and Social Influence." American Economic Journal: Microeconomics 10, no. 3 (August 1, 2018): 124–42. http://dx.doi.org/10.1257/mic.20160190.

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Interaction between decision makers may affect their preferences. We consider a setup in which each individual is characterized by two sets of preferences: his unchanged core preferences and his behavioral preferences. Each individual has a social influence function that determines his behavioral preferences given his core preferences and the behavioral preferences of other individuals in his group. Decisions are made according to behavioral preferences. The paper considers different properties of these social influence functions and their effect on equilibrium behavior. (JEL D11, D83, D91, Z13)

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37

O’Keefe, Daniel J. "Guilt and Social Influence." Annals of the International Communication Association 23, no. 1 (January 2000): 67–101. http://dx.doi.org/10.1080/23808985.2000.11678970.

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38

BURGOON, MICHAEL, and JAMES PRICE DILLARD. "Communication and Social Influence." Communication Research 22, no. 4 (August 1995): 397–401. http://dx.doi.org/10.1177/009365095022004001.

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39

Denrell, J. "SOCIOLOGY: Indirect Social Influence." Science 321, no. 5885 (July 4, 2008): 47–48. http://dx.doi.org/10.1126/science.1157667.

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40

Friedkin, Noah E., and Eugene C. Johnsen. "Social influence and opinions." Journal of Mathematical Sociology 15, no. 3-4 (January 1990): 193–206. http://dx.doi.org/10.1080/0022250x.1990.9990069.

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41

Mason, Winter A., Frederica R. Conrey, and Eliot R. Smith. "Situating Social Influence Processes: Dynamic, Multidirectional Flows of Influence Within Social Networks." Personality and Social Psychology Review 11, no. 3 (August 2007): 279–300. http://dx.doi.org/10.1177/1088868307301032.

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42

Chuang, Chun-Ling, Hong-Lin Tian, and Rong-Ho Lin. "Integrating Certainty Effect and Noninteractive Social Influence Into Impulse Buying." Social Behavior and Personality: an international journal 43, no. 5 (June 13, 2015): 777–93. http://dx.doi.org/10.2224/sbp.2015.43.5.777.

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We integrated certainty effect and noninteractive social influence into impulse buying, and explored the relationships between impulse buying tendency and intention in regard to the impacts of certainty effect and social influence. We selected 2 certainty effects (1-phase and 2-phase discounts) and 3 social influences (number, proximity, and strength) that stimulate consumers' impulse buying intention for a unique product. The participants were 416 students at 5 universities in northern Taiwan. Results of structural equation modeling indicated that impulse buying tendency positively influenced impulse buying intention in terms of certainty effects and social influences; there was a positive relationship between certainty effects and social influences in impulse buying intention; the students preferred 2-phase to 1-phase discounts; and, in descending order, the social influences of strength, number, and proximity impacted impulse buying intention. We concluded that marketers can use certainty effects and noninteractive social influences to stimulate consumers' buying intention.

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43

Zhang, Jing, Jie Tang, Juanzi Li, Yang Liu, and Chunxiao Xing. "Who Influenced You? Predicting Retweet via Social Influence Locality." ACM Transactions on Knowledge Discovery from Data 9, no. 3 (April 13, 2015): 1–26. http://dx.doi.org/10.1145/2700398.

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44

Yap, Hock Yeow, and Tong-Ming Lim. "Social trust: impacts on social influential diffusion." International Journal of Web Information Systems 13, no. 2 (June 19, 2017): 199–219. http://dx.doi.org/10.1108/ijwis-11-2016-0067.

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Purpose This paper aims to present social trust as a variable of influence by demonstrating the possibilities of trusted social nodes to improve influential capability and rate of successfully influenced social nodes within a social networking environment. Design/methodology/approach This research will be conducted using simulated experiments. The base algorithm in research uses genetics algorithm diffusion model (GADM) where it carries out social influence calculations within a social networking environment. The GADM algorithm will be enhanced by integrating trust values into its influential calculations. The experiment simulates a virtual social network based on a social networking site architecture from the data set used to conduct experiments on the enhanced GADM and observe their influence capabilities. Findings The presence of social trust can effectively increase the rate of successfully influenced social nodes by factorizing trust value of one source node and acceptance rate of another recipient node into its probabilistic equation, hence increasing the final acceptance probability. Research limitations/implications This research focused exclusively on conceptual mathematical models and technical aspects so far; comprehensive user study, extensive performance and scalability testing is left for future work. Originality/value Two key contributions of this paper are the calculation of social trust via content integrity and the application of social trust in social influential diffusion algorithms. Two models will be designed, implemented and evaluated on the application of social trust via trusted social nodes and domain-specified (of specific interest groups) trusted social nodes.

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45

Koehler, Katelyn, and Mary C. Broughton. "The effect of social feedback and social context on subjective affective responses to music." Musicae Scientiae 21, no. 4 (September 21, 2016): 479–98. http://dx.doi.org/10.1177/1029864916670700.

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Previous research suggests that music listening influences individual affective responses. However, there is scant research examining how social factors might interact to influence subjective affective responses to music. This study investigates the effects of social feedback and social context on subjective affective responses to music. In a between-subjects experiment, participants (N = 120) listened to unfamiliar music from various genres either alone or with another participant. For each musical example, participants received positive and negative social feedback, derived from a pilot study, or factual album information. After listening to each musical example and reading the provided social feedback or album information, participants reported their subjective valence, arousal, subjective affective intensity, concentration, music liking and familiarity. There was no effect of social feedback on subjective valence responses. Positive and negative social feedback influenced subjective arousal responses positively and negatively, respectively. Subjective affective intensity was not influenced by social feedback. Social context did not influence subjective affective responses to the musical examples. Lower concentration was reported in social listening conditions compared to solitary conditions. Greater familiarity with the musical examples was reported when social feedback was provided. The findings of the present study suggest that social feedback can influence particular affective responses to, and familiarity with, music. However, social listening might reduce concentration, especially in the absence of social feedback. These findings highlight issues warranting consideration for how music is affectively experienced in everyday life, as well as purposely used in varied contexts.

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46

Bulut, Fatma. "BUILDING CONFIDENCE: THE INFLUENCE OF MICROTEACHING ON SPEAKING ANXIETIES AMONG PRE-SERVICE TURKISH LANGUAGE TEACHERS." Global Journal of Humanities and Social Sciences 03, no. 03 (March 2, 2024): 08–14. http://dx.doi.org/10.55640/gjhss-social-327.

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This study investigates the influence of microteaching on alleviating speaking anxieties among pre-service Turkish language teachers. Speaking anxiety is a common concern among language educators, affecting teaching effectiveness and overall classroom dynamics. Through a mixed-methods approach, data were collected from pre-service Turkish language teachers enrolled in teacher education programs. Quantitative measures, including pre- and post-microteaching surveys, were utilized to assess changes in speaking anxieties. Additionally, qualitative insights were gathered through interviews to explore participants' perceptions and experiences regarding microteaching and its impact on speaking anxieties. The findings highlight the significant role of microteaching in building confidence, improving speaking skills, and reducing anxieties among pre-service Turkish language teachers. Implications for teacher education programs and recommendations for future research are discussed.

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47

Hu, Jimin, Shafie Sidek, Azmawani Abd Rahman, and Raja Nerina Raja Yusof. "Navigating influence: Unraveling the impact of micro-influencer attributes on consumer choices in the Chinese social media." Innovative Marketing 20, no. 2 (May 8, 2024): 152–68. http://dx.doi.org/10.21511/im.20(2).2024.13.

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This study aims to explore the relationship between consumer purchasing behavior and key micro-influencer attributes, including knowledge, entertainment value, credibility, and transparency, within the context of Chinese social media platforms. The paper adopts a quantitative approach, employing partial least squares structural equation modeling (PLS-SEM) to analyze the intricate relationships among latent variables. The respondents comprise active users of major Chinese social media platforms, such as Weibo and Xiaohongshu. For primary data collection, 329 respondents were surveyed online, utilizing a convenient sampling method as part of non-probability sampling. Data collection spanned four weeks, and participants were given the option to respond in either English or Mandarin. The findings suggest significant associations between consumer purchasing behavior and micro-influencer attributes. Specifically, knowledge, entertainment value, credibility, and transparency exhibit varying degrees of influence on consumer behavior within the Chinese social media landscape. The p-value for H1, H2, H3, and H7 appeared as 0.000 and shows that these are the highly significant relations, whereas the p-value for H3 (0.019), for H5 (0.001), and for H6 (0.028) shows that these relations play a moderate role in the proposed model. Elucidating the role of key attributes provides valuable insights for marketers and businesses seeking to leverage micro-influencer marketing strategies effectively in this rapidly evolving digital landscape.

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48

Sardak, Sergii, Maxim Korneyev, Anastasiia Simakhova, and Olha Bilskaya. "Global factors which influence the directions of social development." Problems and Perspectives in Management 15, no. 3 (December 4, 2017): 323–33. http://dx.doi.org/10.21511/ppm.15(3-2).2017.02.

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This study identifies global factors conditioning the global problematics of the direction of social development. Global threats were evaluated and defined as dangerous processes, phenomena, and situations that cause harm to health, safety, well-being, and the lives of all humanity, and require removal. The essence of global risks was defined. These risks were defined as events or conditions that may cause a significant negative effect for several countries or spheres within a strategic period if they occur. Global problems were conceptualized. These problems were defined as phenomena, matters, and situations that are not completely understandable, interesting, actual, require solving and regulation, and in addition do not have unified solutions. Current global challenges were analyzed, defined as strategic guidelines that cause transformational changes and are receiving attention from humanity. Global trends were defined as courses of social development. The composition and role of global actors were described, and defined as international organizations, leading states, regional organizations, powerful corporations, institutional investors, large cities, and well-known personalities. The directions of positive courses of social development were developed by means of using the authors’ mechanism of solving the global problematics.

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49

Jonason, Peter K., and Gregory D. Webster. "A protean approach to social influence: Dark Triad personalities and social influence tactics." Personality and Individual Differences 52, no. 4 (March 2012): 521–26. http://dx.doi.org/10.1016/j.paid.2011.11.023.

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50

Yu, Shipan. "The Influence of Social Media on Interpersonal Relationships." Communications in Humanities Research 9, no. 1 (October 31, 2023): 90–97. http://dx.doi.org/10.54254/2753-7064/9/20231126.

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With the rapid popularization and development of social media, the way people socialize in the new digital society is changing. The purpose of this study is to explore the influence of social media on interpersonal relationships, and to explore in depth the relationship dependent on network social platforms, new media, social media and short video social media. While providing diversified communication channels, social media also brings negative effects, such as loneliness and anxiety caused by lack of face-to-face communication. Different types of social media, such as network social platforms, new media and social media, have different influences on interpersonal relationships. The influence of social media on interpersonal relationship is also influenced by individuals social media dependence, usage habits and other factors. The author observes the connection of the social media and interpersonal relationship to have some analyzes and give some advice which may decrease the negative effects and increase the positive effects.

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