Investigating the Design of Social Networking Sites to Examine the Spread of Political Misinformation Using the Uses and Gratifications Theory

Investigating the Design of Social Networking Sites to Examine the Spread of Political Misinformation Using the Uses and Gratifications Theory

Joel Andrew B. Cruz Jr, Ma. Rowena R. Caguiat, Ryan A. Ebardo

Department of Information Technology, De La Salle University

DOI: https://doi.org/10.51244/IJRSI.2024.11120037

Received: 07 December 2024; Accepted: 11 December 2024; Published: 10 January 2025

ABSTRACT

The shift from mass media to personalized content has gained popularity. Activity on social networking sites (SNS) showed that these platforms are designed to encourage social interaction and do not endorse critical thinking. This sentiment aligns with the Uses and Gratifications Theory (UGT), which correlates the motivation to share misinformation on social media to fulfill their needs or gratification, like their desire for social validation or affirmation of their beliefs. Thus, this paper examines the general design of social networking sites and how they transpired during the 2022 Philippine presidential elections. This study proposed hypotheses on how each design element corresponds with the motivational factors influencing the spread of political misinformation. Using Structural Equations Modeling (SEM), the study aims to test these constructs empirically. Results indicated that altruism, socialization, and entertainment, and status-seeking equally correlate as predictors of misinformation because they are embedded with the existing design of SNS. Therefore, addressing the issue would require redesigning these SNS to counteract the spread of fake news.

Keywords: social media, political misinformation, Philippine elections, fake news, Uses and Gratifications Theory

INTRODUCTION

Statistics show that the Philippines remains the world’s social media capital. Reuters Institute surveyed in 2023 and revealed that 86% of Filipinos’ top medium of receiving news is via their smartphones [1]. However, this often creates consequences since SNS use algorithms to tailor content [2].

Brian Amerige, a former Facebook engineer, stated that SNS “are not designed to help your thinking. They’re not designed to answer questions about complex and controversial issues in your life.” Rationalizing this, SNS does not cause misinformation, but its current architecture assists in the spread of true or false information [3].

Drake and Mehta [4] argued that the term “social” in social media denotes that these platforms’ main goal is to promote social interaction. SNS’s central design is really to propagate information and enable content sharing. However, this presents a design flaw wherein it prioritizes sensational content that garners attention over factual accuracy [5]. Posts that evoke strong emotional responses can make anyone susceptible to false claims [6], [7]. Likewise, it is imperative to understand how the behavior of social media users and whether the design of SNS promulgates the spread of misinformation.

Today, social media has become an enabler of the spread of misinformation, and what is more alarming is that SNS cultivates a rapid but anonymous spread of misinformation [8], [9]. Zhou et al. [10] emphasize that it is becoming problematic to distinguish fake from factual news. Therefore, it is necessary to probe how people share fake news [11].

The devastating impact of misinformation can be seen from different aspects like public health, politics, environment, and economic issues [17]. Thus, further studies are necessary to expand the existing literature in this domain. Additionally, the prominence of studies in this research area is highly concentrated in Europe and North America. However, this limits the findings’ applicability to other cultural and social contexts. This geographical bias creates a gap in understanding how misinformation operates in diverse environments [12]. Hence, this paper is framed on localizing the research in the Philippine political environment. Framing the approach in this will contribute to the existing literature in the ASEAN region.

The role of SNS in moderating misinformation is also a critical area of inquiry. Related literature supported the idea that motivational factors (e.g. social interaction, sharing information, seeking status, and looking for information) became triggers for news-sharing behavior on SNS [13][14]. However, the sheer amount of information shared on SNS undermines fact checking. Therefore, hypothesizing why the design of SNS exacerbates the spread of misinformation is a vital objective of this paper.

Aside from this, a person’s intrinsic motivation is also an angle to look at. Users have their own needs and wants like getting entertained, staying informed, or staying a part of a social circle [15][16].  By examining the online behavior of Filipinos in a politically charged environment will shed some light on this phenomenon.

Lastly, to guide these objectives, this research is framed on this central question:

RQ1: Does the design of SNS contribute to the prevalence of political misinformation?

LITERATURE REVIEW

Misinformation and the 2022 Philippine elections

As seen in academic literature, misinformation is examined depending on the author’s intention to mislead their audience [19]. Misinformation refers to deceitful, distorted, or misleading information that does not represent the true state of facts. In retrospect, fake news refers to deliberately fabricated or misleading information designed to appear as legitimate news [20].

Oh et al. [21] state that misinformation happens when people face a scarcity of information they need. Psychology tells us that anxiety is the primary culprit for the spread of why misinformation happens. Supported by Ahmed & Rasul [22], the sheer volume of information shared on social media can overwhelm users. Many users lack the skills or motivation to conduct thorough fact-checking, allowing misinformation to proliferate. Thus, misinformation becomes a norm during an emergency that helps the community understand an unknown situation [23].

With the rising use of social media and journalism, Rappler is a Filipino online news organization created in 2012 whose value proposition is truth-telling and factual reporting [24]. Together with the Google News initiative, Rappler launched the FactsFirstPH project in 2022. This project fact-checked political-related articles published from October 2021 to March 2022. Out of 256 articles they validated, 207 were reported to be false, and 39 were missing context and showed nuances in their claims. These findings indicated how political misinformation became a winning catalyst for candidates with a solid social media presence [25]. Recommendation algorithms heavily influenced the seemingly popularity contest amongst candidates. The significant attempt to use social media for aggressive campaigning amplified the growth of troll farms, fake news, and internet propaganda [26]. The Philippines is a democratic country. Thus, the majority rule applies when Filipinos elect their next leaders. Nevertheless, Rappler’s case study illustrated how social media activity boosted a candidate’s survey performance. Thus, this current study seeks to empirically explore whether a user’s motivational factor and SNS design prompt political misinformation sharing among voters in the Philippines.

Social media design and misinformation

Studies have been published on the design of social networking sites. These authors dissected the general elements found in these SNS and how they can exacerbate the problem of misinformation.

1. Recommendation algorithms

Algorithms are automated tools SNS utilize to suggest content to users, such as organizing content in a feed or encouraging users to explore additional material. SNS algorithms greatly influence users’ experiences, whether online or offline. As a design element, it can do quite a lot of things like (a) introduce individuals to new people and perspectives [27], (b) help users learn about subjects that interest and keep them updated [5], or (c) even raise awareness about social movements [28]. As a drawback, the growing number of items shared on SNS caused an information overload for users [29]. Cognitive overload can lead to confirmation bias which exhibits an echo chamber effect where users are primarily exposed to information that only resonates with their pre-existing beliefs [6]. Consequently, this makes sharing false information much easier [11].

2. Like button

SNS use this button to mark favorite items, rate satisfaction, or establish a sense of approval. In retrospect, the Like button encourages faster engagement of posts that appeal to the viewer. People post frequently when their posts receive positive feedback. The dopamine-induced effect encourages them to do so [30]. Thus, it creates a cycle in which the poster will publish more similar items regardless of whether they are genuine or fake [4].

3. Share button

SNS allow technologies for sharing photos, videos, and web links. The study conducted by Ham et al. revealed reasons for sharing information [31]. His findings found that motivation to establish a social presence, engage in social conversation, and find easy connections were the causes that can be correlated to why people wanted to share on social media. Thus, these results uncover that the share button enables status-seeking behavior more than truth-seeking behavior [4].

Theoretical paradigm and formulation of hypothesis

This study was formulated using the Uses and Gratifications Theory (UGT). Developed in the late 1950s, UGT emphasized the rationale behind why people purposely choose a certain media and how they utilize its features to satisfy their needs and wants [32].

UGT asserts that media use is affected by both social and psychological factors. Social factors include peer pressure, family dynamics, and cultural norms. Meanwhile, psychological factors encompass individual traits, attitudes, and motivations [33].

The early formulation of the UGT theory focused on traditional media; however, its relevance has recently expanded to include research on the internet, particularly in the realm of social media [13].

Similarly, some studies published dissected the gratification motives behind sharing fake news on social networking sites (SNSs). Wei et al. [34] presented that people are not passive receivers of information but instead actively involved in choosing media that fulfills their needs and goals. Thus, with the help of UGT, correlating the effect of SNS design to rationalize individual gratifications is well-fitted.

UGT’s primary assumption is that users pick the best one that gratifies their current needs, whether keeping informed, connecting with others, or feeling entertained. Thus, the present study formulates an empirical model that links each design element with the corresponding gratification factors people experience when using SNS.

The table below summarizes previous research on the gratification factors that were used as a basis for the hypothesis formulated in this study

TABLE I. Summary of gratification factors used in previous studies

Factor	Description	Citation
Information Sharing Gratification	There is a satisfaction from gaining a sense of purpose through helping others.	Crist, S. (2024)
Information Seeking Gratification	There is satisfaction from seeking new knowledge or staying informed	Lee et al. (2018)
Socialization Gratification	There is satisfaction gained from interacting with others	Apuke & Omar (2020)
Entertainment Gratification	There is a feeling of pleasure from consuming or sharing content	Mäntymäki & Islam (2016)
Self-Promotion Gratification	There is a need to show that they are capable, intelligent, or talented	Thompson et al. (2019)

Altruism and share button

Altruism denotes the behavior of offering something to others with no anticipation of receiving anything in exchange. In terms of this study, altruism can be understood as disseminating information and news without considering any personal gain. A Filipino’s core value is being helpful to others regardless of circumstance [35]. Consequently, the relationship between altruism and sharing fake news is expected since a share button exists on almost any SNS. Thus, it can be hypothesized that:

H1. Altruistic behavior will be positively associated with the spread of political misinformation due to the presence of a share button.

Socialization and recommendation algorithm

The drive for socialization pertains to the desire for an individual to be connected to his community [36]. In the context of this paper, socializing entails the need to form and nurture existing relationships and engage with others on social media. As part of a social network, Wasserman and Madrid-Morales [37] posit that people tend to trust information shared by family members or friends more than strangers. Moreover, it is also a typical Filipino value to be family-oriented. Thus, in the lens of misinformation, Chang et al. [38] discovered a strong correlation between misinformation sharing and socialization gratification. Hence, given that recommendation algorithms suggest similar content from a user’s feed and virtual friends, it is empirical to propose that:

H2. Socialization motivation will be positively associated with the spread of political misinformation because of recommendation algorithms.

Entertainment and like button

Aside from getting informed of current events, social media is used to satisfy the desire for entertainment [39]. Although those who aim to inform and help others are concerned about the credibility and reliability of the information they disseminate [40], those looking for enjoyment may not experience the same sense of duty. This scenario can be seen when a trending post receives many likes. Some have suggested that although humor is generally beneficial, focusing on entertainment as an objective overlooks the authenticity of the shared information [41]. Drawing from this idea, it can be hypothesized that:

H3. Seeking entertainment will be positively associated with the spread of political misinformation with the help of a like button.

Status-seeking and share button

As social beings, people may want to establish an online presence through their social media. Similarly, this could be applied to news sharing online, where people attempt to share news to demonstrate that they are competent, socially aware, or intelligent [42]. When personal reputation is on the line, people tend to be more cautious about what they share. This leads individuals to verify the information they share more thoroughly [43]. Based on this argument, it can be hypothesized that:

H4. Desiring to have a good status image on social media is negatively associated with spreading political misinformation.

Fig. 1. Proposed Hypotheses

METHODOLOGY

Research design, data collection, and participants of the study

This study adopted a quantitative research approach. According to Creswell and Cresswell [44], this type of research involves an examination of the relationships among variables, which are measured through instruments and later analyzed using statistical procedures.

As a research instrument, an online survey (using Google Forms) was cascaded to test the hypotheses that had been developed. The study sample was Filipino voters who participated in the recently concluded 2022 election and are active users of Facebook or X.  These are Filipinos’ leading SNS [1]. Thus, a snowball sampling through Facebook was performed to recruit participants. The survey link was posted on Facebook, and participants were instructed to complete the survey and share the link within their social circle. Given that this is research in progress, the survey was piloted to only 30 respondents.

Nonetheless, ethical research was also observed. Respondents must first accept the consent form that outlines that they fully understand and agree to the needs of the study. Participation was voluntary, and the respondent’s confidentiality was protected by anonymizing their identity. Table 1 summarizes the demographic profile of the respondents.

TABLE I. Demographic Profile of Respondents

Category	Distribution (%)
Age 18-24 25-34 35-44 45-54 55-64 65+	26.30% 27.30% 23.20% 15.40% 5.30% 2.50%
Profession Employed Full-time Employed Part-time Student Retired Unemployed	46.00% 10.00% 31.00% 0.00% 13.00%
Education High school Bachelor’s Degree Master’s Degree PhD	38.90% 45.80% 15.30% 0.00%
Social media mostly used Facebook X (formerly Twitter) Tiktok Instagram Others	43.33% 10.00% 16.67% 23.33% 6.67%

The instrument was a close-ended questionnaire that included demographic variables, the medium of receiving news, and social media usage. Participants also answered a five-point Likert-scale survey and displayed to what extent they agreed or disagreed with the statements provided. Reliability describes whether the variables used in the study are consistent. A common standard to evaluate this is through Cronbach’s Alpha. The reliability of 0.764 using Cronbach’s alpha value exceeded the 0.7 threshold [45]. Therefore, this exhibited that the constructs used in this study are acceptable.

Data Analysis

The research used Jamovi, an open-source statistical tool, to conduct descriptive statistics. On the other hand, the construct validity of an instrument assesses how well it captures the desired outcomes of a study. It means that the construct’s validity will be jeopardized if any constructs are missing or if unnecessary components are present. Furthermore, one way to establish construct validity is to establish convergence.

Confirmatory Factor Analysis (CFA) was performed to quantify the convergence of the questionnaire used in the study. Each predictor of the spread of misinformation was used as a construct. Afterwards, the Comparative Fit Index (CFI) validated relationships with these constructs.

Confirmatory Factor Analysis

CFA is a statistical procedure utilized to validate the factor organization of a group of observed variables. Its goal is to confirm whether the data collected fits a hypothesized measurement based on an existing theory or previous research [46]. Each predictor of the spread of misinformation was used as a construct using CFA. Afterwards, the Comparative Fit Index (CFI) validated relationships with these constructs.

From the research instrument, multiple constructs were considered. Afterward, each factor from the questionnaire was assigned by corresponding factor loading and Composite Reliability (CR) computation. Lastly, the model’s validity is assessed by evaluating the model fit thresholds. Criteria like factor loadings (should be at least 0.7 or higher) and fit indices such as Chi-square, Root Mean Square Error of Approximation (RMSEA), and Comparative Fit Index (CFI) should at least be 0.90.

TABLE II. Model Fit Indices

Fit indices				95% Confidence Intervals
X²	df	CFI	RMSEA	Lower	Upper
648	146	99.2	0.356	0.33	0.384

Fig. 2. Path Analysis

Structural Model

To test the hypothesis presented, the structural equation model (SEM) was used for the analysis. Specifically, Partial Least Squares was used to ascertain the significance of the paths. [45].

DISCUSSION OF RESULTS

Presentation of Findings

To test the hypotheses presented, the result of SEM is summarized below:

TABLE III. Structural Model

Hypothesis	Path β	p-value	Decision
H1	0.042	<0.001	Supported
H2	0.559	<0.001	Supported
H3	0.315	<0.001	Supported
H4	0.023	<0.001	Supported

Interpretation of Findings

The study revealed that altruism, socialization, and entertainment relate to the dependent variable of sharing political misinformation. Among these constructs, altruism had the most significant effect on fake news sharing compared to the other constructs. This finding echoes past studies on the factors of the spread of fake news [7]. Altruism is an act of assisting others through the sharing of information without expecting anything in return. As [47] investigated, altruism is the primary motivation for individuals to provide information voluntarily. In retrospect, the possible reason for this is that during the 2022 election, people are eager to disseminate information they believe could aid others in selecting the best candidate to serve the Philippines. It is easy and convenient for everyone with a single button click. However, this was done without considering the authenticity of the information.

The second significant construct is socialization. This result shows a positive connection between socialization and sharing of political misinformation. This finding illustrates that Filipinos often propagate unverified and misleading information on social media. Social media became a channel for users to keep updated, exchange advice, and express political views. Though this is common, recommendation algorithms on SNS govern content visibility. Consequently, these algorithms tailor-fit to the content the user wants to see. This limits the exposure of an individual to diverse perspectives. Similarly, it becomes a pitfall if the narrative on a user’s feed is surrounded by fake news from his social network.

The third factor from the study is users seeking entertainment. Results showed a positive association between political misinformation and entertainment. Reflecting on this result will

draw on the idea that leading candidates during the 2022 election had multiple support pages bearing their names [26]. Reasoning this out, Filipinos have a creative sense of humor and often like or share news stories, photos, or memes that amplify the chance of winning their better candidate.

As the last factor, the status-seeking and spread of misinformation hypothesis was supported. This outcome backs the idea of a past study that asserts that people motivated to establish an online presence will likely want more likes, shares, and recognition on their posts [48]. This status-seeking behavior was also apparent during the win of the past Philippine president, Rodrigo Duterte, in 2016. Celebrity endorsers were also prominent in disseminating misinformation and propaganda during this time. Thus, this construct is not supported since the spread of political misinformation disregards the idea that avid supporters of a candidate seemingly lack the fear that their image will be destroyed in the event the news they share is fake.

LIMITATIONS AND FUTURE WORK

The limitation of this study is that it only presents preliminary results from a relatively small sample size. Thus, future work should increase the samples collected to gain a more robust statistical output. Furthermore, the gathered information through the online survey depends on the respondents’ awareness and account. As a result, the respondents’ answers may not accurately reflect the actual circumstances. To address this, a focus group discussion can be created to verify the information shared during the survey administration.

CONCLUSION

The spread of misinformation on social media is a multi-dimensional issue attributed to the design of SNS, psychological factors, and the inadequacies of current countermeasures. Addressing these issues requires a comprehensive approach that enhances the accuracy of information shared on these SNS and encourages critical thinking among its users. Therefore, as social media continues to evolve and become part of society, many strategies to combat misinformation are also necessary.

REFERENCES

1. Chua, Y. (2023, June 14). Digital News Report 2023. Philippines. University of Oxford. https://reutersinstitute.politics.ox.ac.uk/digital-news-report/2023/philippines
2. Lazer, D. et al. (2018). The Science of Fake News,  Science 359 (6380) 094-1096.
3. Amerige, B. (2022, February 25). John Drake [YouTube Channel]. YouTube. Voices of eCommerce, Ep. 13 – Brian Amerige of Thoughtful and rethinking Social. Retrieved on https://youtu.be/BIH58PcKrGo?feature=shared
4. Drake, J. & Mehta, N. (2024). A cognitive look at social media’s architecture and its impact on the spread of misinformation. Americas Conference on Information Systems Proceedings 7. https://aisel.aisnet.org/amcis2024/social_comp/social_comput/7
5. Boczkowski, P. J., Mitchelstein, E., & Matassi, M. (2018). News comes across when I’m in a moment of leisure: understanding the practices of incidental news consumption on social media. New Media &Amp; Society, 20(10), 3523-3539. https://doi.org/10.1177/1461444817750396
6. Zhao, Y., Zhu, S., Wan, Q., Li, T., Zou, C., Wang, H., & Deng, S. (2022). Understanding how and by whom covid-19 misinformation is spread on social media: coding and network analyses. Journal of Medical Internet Research, 24(6), e37623. https://doi.org/10.2196/37623
7. Tucker, J., Guess, A., Barberá, P., Vaccari, C., Siegel, A., Sanovich, S., & Nyhan, B. (2018). Social media, political polarization, and political disinformation: a review of the scientific literature. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3144139
8. Vicario, M. D., Quattrociocchi, W., Scala, A., & Zollo, F. (2019). Polarization and fake news. ACM Transactions on the Web, 13(2), 1–22.
9. Liang Wu, Fred Morstatter, Kathleen M. Carley, and Huan Liu. 2019. Misinformation in Social Media: Definition, Manipulation, and Detection. SIGKDD Explor. Newsl. 21(2), 80–90. https://doi.org/10.1145/3373464.3373475
10. Zhou, P., Yang, XL., Wang, XG. et al. A pneumonia outbreak associated with a new coronavirus of probable bat origin. Nature 579, 270–273. https://doi.org/10.1038/s41586-020-2012-7
11. Pennycook, G., McPhetres, J., Zhang, Y., Lu, J., & Rand, D. (2020). Fighting covid-19 misinformation on social media: experimental evidence for a scalable accuracy-nudge intervention. Psychological Science, 31(7), 770-780. https://doi.org/10.1177/0956797620939054
12. Muhammed T, S., Mathew, S.K. The disaster of misinformation: a review of research in social media. Int J Data Sci Anal 13, 271–285 (2022). https://doi.org/10.1007/s41060-022-00311-6
13. Thompson, N., Wang, X., Daya, P., 2019. Determinants of News Sharing Behavior on Social Media. Journal of Computer Information Systems 00(00), 1–9. https://doi. org/10.1080/08874417.2019.1566803
14. Lee, C., Ma, L., Goh, D., (2011). Why do people share news in social media? Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 6890 LNCS, 129–140. https://doi.org/10.1007/978-3-642-23620-4_17
15. Khan, M., Idris, I., 2019. Recognise misinformation and verify before sharing: a reasoned action and information literacy perspective. Behaviour and Information Technology. https://doi.org/10.1080/0144929X.2019.1578828.
16. Quan-Haase, A., & Young, A. L. (2010). Uses and Gratifications of Social Media: A Comparison of Facebook and Instant Messaging. Bulletin of Science, Technology & Society, 30(5), 350-361. https://doi.org/10.1177/0270467610380009
17. Chen et al. (2023, April). Spread of misinformation on social media: What contributes to it and how to combat it. Computers in Human Behavior. Vol 141. https://doi.org/10.1016/j.chb.2022.107643
18. Bella, E., Allen, C., & Lirussi, F. (2021). Communication vs evidence: what hinders the outreach of science during an infodemic? a narrative review. Integrative Medicine Research, 10(4), 100731. https://doi.org/10.1016/j.imr.2021.100731
19. Wardle, C. & Derakhshan, H. (2017). Information Disorder: Toward an Interdisciplinary Framework for Research and Policy Making. C.o. Europe (ed.). Council of Europe.
20. Appan & Browne, G. (2012). The Impact of Analyst-Induced Misinformation on the Requirements Elicitation Process. MIS Quarterly 36(1). pp. 85-106
21. Oh, H., Labianca, G., & Chung, M. (2006). A multilevel model of group social capital. Academy of Management Review, 31(3), 569–582.
22. Ahmed, S. and Rasul, M. (2022). Social media news use and covid-19 misinformation engagement: survey study. Journal of Medical Internet Research, 24(9), e38944. https://doi.org/10.2196/38944
23. Liu, F., Burton-Jones, A., Xu, D (2014). Rumors on social media in disasters: extending transmission to retransmission. PACIS 2014. Proceedings. 49. https://aisel.aisnet.org/pacis2014/49
24. Rappler (2021, February 22). Rappler’s mission statement. Rappler. Retrieved from https://www.rappler.com/about/policies/mission-statement-journalism-communities-technology/ on October 20, 2024.
25. Eusebio, G. (2023, May 23). [ANALYSIS] Fake news and internet propaganda, and the Philippine elections: 2022. Rappler. Retrieved from https://www.rappler.com/technology/features/analysis-fake-news-internet-propaganda-2022-philippine-elections/ on October 20, 2024
26. Baizas, G (2021, July 17). Investigating troll farms: What to look out for. Rappler. Retrieved from https://www.rappler.com/newsbreak/iq/investigating-troll-farms-what-to-look-out-for/on November 26, 2024.
27. Cheng, F. and Wu, C.. (2010, June). Debiasing the Framing Effect: The Effect of Warning and Involvement. Decision  Support Systems 49(3). pp. 328-334.
28. Poell, T., van Dijck, J. (2015). Social Media and the Transformation of Public Space. Social Media + Society 1(2). https://doi.org/10.1177/2056305115622482
29. Koroleva, K., Krasnova, H., & Gunther, O. (2010). Stop Spamming Me! – Exploring Information Overload on Facebook. AMCIS. 2010 Proceedings, Lima, Peru.
30. Burhan, R., & Moradzadeh, J. (2020). Neurotransmitter Dopamine (Da) and Its Role in the Development of Social Media Addiction. Journal of Neurology & Neurophysiology 11(7). 507.
31. Ham, C., Lee, J., Hayes, J, & Bae, Y. (2019). Exploring Sharing Behaviors across Social Media Platforms. International Journal of Market Research 61(2). 157-177
32. Katz, E., Blumler, J. G., & Gurevitch, M. (1973). Uses and gratifications research. Public Opinion Quarterly, 37(4), 509.
33. Crist, S. (2024). To Share or not to Share: Understanding Message Features that Motivate Social Media Users to Share Misinformation [Doctoral dissertation] Regent University.
34. Wei et al. (2024). The uses and gratifications (U&G) model for understanding fake news sharing behavior on social media. The Journal of Academic Librarianship 50(5). https://doi.org/10.1016/j.acalib.2024.102938
35. Gallimore, D. (2023, February 17). Understanding Filipino traits, values, and culture. The Source. Retrieved from https://www.outsourceaccelerator.com/articles/filipino-traits-and-values/ on November 27, 2024
36. Lee, C., Ma, L., Goh, D., (2011). Why do people share news in social media? Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 6890 LNCS, 129–140. https://doi.org/10.1007/978-3-642-23620-4_17.
37. Wasserman, H., & Madrid-Morales, D. (2019). An Exploratory Study of “Fake News” and Media Trust in Kenya, Nigeria and South Africa. African Journalism Studies, 40(1), 107–123. https://doi.org/10.1080/23743670.2019.1627230
38. Chang, S., Liu, A., and Shen, W. (2017). User trust in social networking services: a comparison of Facebook and LinkedIn. Computers in Human Behavior, Vol. 69, pp. 207-217. https://doi.org/10.1016/j.chb.2016.12.013
39. Mäntymäki, M., & Islam, N. (2016). The Janus face of Facebook: positive and negative sides of social networking site use. Computers in Human Behavior, Vol. 61, pp. 14-26. https://doi.org/10.1016/j.chb.2016.02.078.
40. Vuori, V. and Okkonen, J. (2012). Knowledge sharing motivational factors of using an intra‐organizational social media platform. Journal of Knowledge Management, Vol. 16 No. 4, pp. 592-603. https://doi.org/10.1108/13673271211246167
41. Islam, A., Laato, S., Talukder, S. and Sutinen, E. (2020). Misinformation sharing and social media fatigue during COVID-19: an affordance and cognitive load perspective. Technological Forecasting and Social Change, Vol. 159. p. 120201. https://doi.org/10.1016/j.techfore.2020.120201
42. Thompson, N., Wang, X., Daya, P., 2019. Determinants of News Sharing Behavior on Social Media. Journal of Computer Information Systems 00(00), 1–9. https://doi. org/10.1080/08874417.2019.1566803
43. Talwar, S., Dhir, A., Kaur, P., Zafar, N., Alrasheedy, M., 2019. Why do people share fake news? Associations between the dark side of social media use and fake news sharing behavior. Journal of Retailing and Consumer Services 51, 72–82. https://doi.org/10.1016/j.jretconser.2019.05.026.
44. Creswell, J. & Creswell, J.D. (2023). Research design. Qualitative, quantitative, and mixed method approaches. Sixth Edition. SAGE Publication Ltd. United States of America
45. Hair, J.F. and Sarstedt, M. (2019). Factors versus composites: guidelines for choosing the right structural equation modeling method. Project Management Journal 50(6). pp. 619-624. https://doi.org/10.1177/8756972819882132
46. Hoyle, R. (2004). Confirmatory factor analysis. Encyclopedia of social science research methods. Thousand Oaks, CA: Sage Publications.
47. Ma, W. & Chan, A. (2014). Knowledge sharing and social media: altruism, perceived online attachment motivation, and perceived online relationship commitment. Computers in Human Behavior, Vol. 39, pp. 51-58. https://doi.org/10.1016/j.chb.2014.06.015
48. Apuke, O. & Omar, B. (2020a). Fake news and COVID-19 : modelling the predictors of fake news sharing among social media users. Telematics and Informatics. p. 101475. https://doi.org/10.1016/j.tele.2020.101475
49. Kenton, W. (2024, August 28). What is the Pearson Coefficient? Definition, history, and benefit. Retrieved from https://www.investopedia.com/terms/p/pearsoncoefficient.asp on December 02, 2024.