Examining how to mitigate the implications of political fake news on social media during U.S. presidential elections

 Corresponding author: Rayan Soti Copyright © 2025 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution License 4.0. Examining how to mitigate the implications of political fake news on social media during U.S. presidential elections Rayan Soti * Monte Vista High School, AP Research, Danville, California, United States of America. World Journal of Advanced Research and Reviews, 2025, 27(03), 1285-1293 Publication history: Received on 05 August 2025; revised on 11 September 2025; accepted on 13 September 2025 Article DOI: https://doi.org/10.30574/wjarr.2025.27.3.3188 Abstract This paper explores how political fake news on social media can be mitigated to reduce its effects on societal voting behavior during presidential elections. From the previous literature, which discusses the prevalence and impact of fake news on voters, this paper aims to address the lack of research on effective ways to combat political fake news on social media. Through a quantitative study, this research tested the effectiveness of a warning sign in helping participants detect misinformation. The results found that the warning sign was effective; however, due to a small sample size and other limitations, there is a need for future researchers to test this. The research calls to solve problems prevalent on social media around the world. Keywords: Political fake news; Social media; Voting Behavior; Warning Signs; Misinformation 1. Introduction The spread of political fake news on social media platforms has become a growing phenomenon, especially around presidential elections. Facebook, Twitter, and Instagram platforms allow quick spreading of information, often transmitting misleading or inaccurate information among users and swaying voting intentions through biased assumptions. Research points to fake news potentially engaging confirmation bias, altering candidate appraisal, and potentially deciding election outcomes. Despite increasing awareness, effective counteractions against political fake news are few, and social media users continue to be vulnerable to misinformation. Warning social media users of possible fake news and reducing its effect on voting intentions in the United States is the aim of this work. Implementing a new warning sign and finding varied demographic-level user responses, this paper tries to find actionable solutions for reducing the harmful effects of political misinformation and promoting wiser voter decision-making. 2. Literature review 2.1. Search Strategies The sources were located primarily through Google Scholar and JSTOR with a minimum requirement of being peerreviewed. The keywords used while researching included: fake news, misinformation, disinformation, presidential election news, social media, voting behavior, and warning signs. 2.2. The Role of Social Media The world of social media platforms has long been proven to be a major issue when it comes to the spread of political fake news during presidential elections. Platforms such as Facebook, Twitter, Instagram, and others are all designed to engage their users for long periods of time; however, they contain potentially harmful news, exposing people to biased World Journal of Advanced Research and Reviews, 2025, 27(03), 1285-1293 1286 thoughts and thoughts. Professor Hunt Allcott and professor Matthew Gentzkow from Stanford University go over some reasons why political fake news seems to be commonly disseminated on Facebook and Twitter. They state, “the fixed costs of entering the market and producing content are vanishingly small,” which means that spreaders of fake news and users themselves are at a much higher exposure to fake news considering it’s easy to create and share content (Allcott and Gentzkow, 2017, p. 221). The researchers also explained how from the 2016 presidential election, they suggest “that Donald Trump would not have been elected president were it not for the influence of fake news” (Allcott and Gentzkow, 2017, p. 212). This highlights the significance of political fake news, as it shows the role of social media influencing voting behavior, which was one of the biggest contributors to the spread of fake news. The researchers also conducted a post-election survey from 2016 as a part of their methods, and found that their participants “spen[t] 66 minutes per day reading, watching, or listening to election news. … Of this, 25 minutes (38 percent) was on social media” (Allcott and Gentzkow, 2017, p. 223). This highlights the substantial amount of time which users spend on social media, which increases their exposure to fake news. Professor Nir Grinberg and others also discuss the role fake news played during the 2016 election on Twitter. Their research highlighted how the exposure to fake news on Twitter can influence participants’ beliefs, even if they don’t fully interact with the platforms. To understand this, the authors study involved Twitter accounts who were registered to vote, and they compared this sample of voters to another sample which helped them conclude how Twitter users made up a large proportion of voters during the 2016 election. The authors also compiled multiple fake news websites posted by sources such as Buzzfeed and Politifact to compare and help define fake news. For Snopes.com by itself, the researchers brought in “over 500 domains that were … found containing false information” (Grinberg et al., 2019, p. 17). This highlights the large scale of fake news websites that were present during the 2016 election. 2.3. Influence on Voting Behavior The spread of political fake news not only exposes users on social media to harmful views, but also influences their voting behavior of candidates during presidential elections. Ceren Budak from the University of Michigan discusses how fake news content from 2016 influenced voting behavior, specifically in support for candidates. In their study, the researchers analyzed news outlets from Twitter, and found a few key points. One of them being how “The prevalence of fake news increased over time” (Budak, 2019, p.139). This means that as the 2016 election approached, the spread of fake news on Twitter according to Budak only worsened and became more frequent, thus leading to a wider range of implications. The researchers also found a “strong correlation between candidate favorability and fake news production and consumption” (Budak, 2019, p. 139-140). This means that as more and more fake news was being created and viewed by users on social media mainly, this caused users’ beliefs of certain candidates to change, causing them to vote differently during the election than they previously would have if they had not seen the false information. This highlights the impact of fake news, as Budak demonstrates in the discussion of his research how there was a widespread amount of fake news about Hillary Clinton by conservative voters to aid Trump in his race for presidency. Patricia Moravec and others from Indiana University explored the effects of asking their participants to assess articles to see if this changes how the participants themselves rate articles. The researchers previously state how “As argued in H1, when users first see an article, their System 1 will produce an instant assessment of its believability based on confirmation bias” (Moravec et al., 2022, p. 9). This means that as people look at articles, they align with it if it correlates with their prior beliefs, without any first consideration on if it’s real or fake. From the 68 participants ages 18-24 brought in, they “viewed 42 news headlines and reported the believability of each article. … 16 were designed to appeal to politically left-leaning participants and 16 to right-leaning participants … There were also 10 that may be of special interest to our participants because of the locality of the issues the headlines described” (Moravec et al., 2022, p. 13). From the study, the researchers found that “participants were more likely to believe articles that they agreed with,” which proves how confirmation bias is an important factor which affects users on social media (Moravec et al., 2022, p. 19). This has severe implications, as it causes users to be more inclined with it, trust it, and share it, furthering the negative implications of fake news influencing voting behavior. Both Moravec et al. and Budak highlight the impactful role of confirmation bias in influencing voting behavior, specifically during the 2016 election. Although Budak’s research focuses on Twitter content over time and its effects, Moravec et al.’s experimental study allowed for the researchers to see other possible variables which can contribute to the influence of voting behavior. However, both studies don’t discuss any potential solutions for this problem, leaving room for ways to combat political fake news to reduce voting behavior on social media. 2.4. A Potential Solution With the spread of political fake news on social media increasing, this leaves questions about how to mitigate this problem. Quétier-Parent et al. (2023) surveyed roughly 8000 participants from 16 different countries, gathering their World Journal of Advanced Research and Reviews, 2025, 27(03), 1285-1293 1287 thoughts on how disinformation was possibly affecting the upcoming elections within their countries. The researchers found that 50% of the participants trusted the news they saw on social media, with another 29% being neutral by neither trusting nor distrusting. Further in the study, the researchers asked the users if disinformation would affect voting behavior, and 87% agreed on this. However, the authors state how “citizens themselves could take stronger actions to address the growing phenomenon of online disinformation” (Quétier-Parent et al., 2023). This means that the researchers’ solution is to rely on the users to address disinformation on their own, which isn’t effective. Their study lacks a possible solution to address this disinformation. However, Björn Ross and others from the University of Duisburg-Essen provide a potential solution to mitigate this problem, warning signs. The researchers reference in their previous literature how “The intention of providing a warning is to affect human behaviour by, for instance, helping the consumer make the right decisions” (Ross et al., 2018, p. 3). From this, the researchers conducted a study by providing their participants with news articles with three conditions, articles with no warning sign, articles with Facebook’s warning sign, and articles with the authors’ creation of a warning sign. However, their results showed that there was not a significant difference between the assessment of an article with Facebook’s warning sign and the authors’ more complex version. 2.5. Research Gap At the end of his study, Budak (2019) stated how she believes “further research on identifying and combating such problematic content is vital to the health of our democracy” (p. 148). Quétier-Parent et al. (2023) discussed how their participants need to take bigger actions to address the problem of fake news, however they don’t provide evidence proving that this would be an effective solution to mitigate the impact of fake news on voting behavior. And although Ross et al. (2018) discuss the possibility of implementing warning signs to combat fake news, they find no significant difference between theirs and Facebook’s, which both have been proven to be ineffective against fake news. As neither study provides an effective solution to this issue, my research gap lies in the lack of research in effective ways to combat political fake news on social media to reduce its implications on voting behavior. This leads to the research question: What can be done to mitigate the spread of political fake news on social media, thereby reducing its impact on voting behavior during presidential elections within the United States? 3. Methods 3.1. Quantitative Survey For the methodology of my study, I decided to conduct a quantitative survey. I required participants to be aged 16+ since I’m focusing on the effects fake news has on voters, so I included the age groups 16-17, 18-39, 40-64, and 65+. Although 16 and 17-year-olds are not able to vote for presidential elections, I included this group in the study because they represent a majority population of users on social media who can be affected by fake news. All individuals signed a consent form allowing them to participate in the study (see Appendix A). The survey was created on google forms, and included three primary sections allowing me to gather information related to demographics, social media platforms, and the effectiveness of a warning sign to combat political fake news (see Appendix B). The survey utilized a 5-point Likert scale. Data collection included spreading the survey through subreddits and emailing a professor of political science to distribute the survey in his class. Data analysis included comparative analysis to determine the effectiveness of the warning sign and other variables. I decided to go with a quantitative approach rather than qualitative to primarily test the effectiveness of a warning sign, which qualitative data wouldn’t have been able to provide. The goal of the study was to address the lack of research on effective ways to combat political fake news on social media. 3.1.1. Demographic/Political Questions For the first section of my survey, I included questions revolving around demographic and political ideas. This included the participants’ age, gender, education, political affiliations, and how often they vote if they were 18 or above (see Appendix B). I was inspired of these questions from research by Ahmad and Murad (2020), where their study analyzed social media’s impact on panic during the COVID-19 pandemic in Kurdistan. I included these variables to determine any trends that correlated to their perceptions of fake news. This could mean that a person with a lower education could be more likely to fall for fake news than a person who received a higher education. The survey also included a “Prefer not to say option” for most questions, thus allowing participants to not feel forced to share any information they didn’t want to. By examining instances such as these, my goal was to find if any of these variables played any role in their susceptibility to fake news on social media. World Journal of Advanced Research and Reviews, 2025, 27(03), 1285-1293 1288 3.1.2. Social Media Consumption and Trust Questions For the second section of my survey, I decided to ask my participants about their consumption and trust of society media. The questions included topics such as considering multiple news sources, accuracy in news, social media platforms, and asking whether these platforms need to implement methods to combat fake news (see Appendix B). This section was inspired by the research of Quétier-Parent et al. (2023), with their study focusing on how people felt about disinformation possibly affecting the upcoming election within their countries. Their methodology made me expand my research to not only work on examining my participants’ concerns about fake news, but also their trust in different news sources and how effective social media platforms are to combat fake news. Additionally, to understand how much individuals trusted social media, I asked the participants to rate their confidence in the accuracy of the news they encountered on social media and other news sources to see if they played any role in how fake news was spread. By doing so, I aimed to assess whether certain platforms included more or less fake news than others. Since I use social media as well, it was important to address my bias about social media platforms since I don’t use Facebook or Twitter. I asked a question in the survey about which social media apps the participants used the most, and included all apps as options even if I wasn’t aware of them, since it could be a significant factor in the spread of fake news. By doing this, I determined whether trust in social media has shifted over time and how the participants’ felt about fake news. 3.1.3. Political Fake News Experiment For the final section of the survey, I analyzed how effective my own utilization of a warning sign was to combat political fake news. Ross et al. (2018) conducted a study which compared the effectiveness of a warning sign they created to Facebook’s warning sign which found that no condition was effective to combat fake news. I decided to test a different warning sign to see its effectiveness to combat political fake news. Currently, Facebook’s warning sign allows users to proceed to fake news, with only one message being shown until the user either clicks away or continues to the source. This means users on Facebook can be exposed to fake news with no warning. From this, I decided to make my own utilization of a warning sign, which will supposedly be placed under all news sources detected by third-party fact checkers. My study tested the effectiveness of a warning sign I created by presenting my participants with political fake news (see Appendix D). I presented eight social media posts in a random order total from Twitter and Instagram (see Appendix B). Four of the posts were fake, and the other four were real. Each social media post was about current and previous presidential candidates including Barack Obama, Hillary Clinton, Donald Trump, Kamala Harris, and Joe Biden. I chose these presidents because they have received the most media attention across social media within past elections, making it easier to find real and fake news about them. For two of the fake posts, I placed the warning sign at the bottom of each post. The first three posts were fake, the fourth one was real, the fifth post was fake, and the last three posts were real. Questions utilized a 5-point Likert scale, with the options ranging from 1 - Completely Fake to 5 - Completely Real. By seeing the data in a range of one to five, I determined how much my participants felt about a post being fake or real. This allowed me to see the effectiveness of the warning sign, and whether or not it could help the participants be more skeptical of the news they were viewing. By comparing the responses between the social media posts, whether fake or real, I saw how effective the warning sign was to help the participants detect political fake news. By conducting this experiment, I added to the scholarly conversation about combating misinformation with warning signs. 3.1.4. Ethicality Considering that my participants were exposed to potentially harmful posts, it was necessary to ensure that none of my participants were negatively influenced. A previous study conducted with fake news led to 87.3% of a total population of 119,982 participants to not be aware that they were viewing misinformation (Loos and Nijenhuis, 2020). To counteract this, I included a post-survey message at the end of the survey (see Appendix C). This message specified which of the posts were fake, which were real, and included a link to each post. By doing this, I ensured that no participants were harmed by the content they were exposed to. 3.2. Data Collection 3.2.1. Step by Step Process To begin my data collection, I decided to first disseminate my survey on subreddit communities on Reddit. After creating an account, I found out that I had to wait a week before spreading the survey on the subreddits. I used this time to my advantage by creating a document and finding different subreddits relating to my research such as r/PoliticalDiscussion or r/fakenews. I chose these reddit groups specifically as they discuss information about fake news, which is relevant to the theme of my research. After waiting a week, I posted my survey to some of these subreddits (see Appendix E). However, most of the posts were deleted due to the account status being new. This made me search for subreddits with World Journal of Advanced Research and Reviews, 2025, 27(03), 1285-1293 1289 smaller populations and less restrictions such as r/Presidentialpoll, r/Republican, and r/Democrat which allowed me to spread my survey further. However, after waiting some time, my survey received very few responses. This was the biggest problem I faced during my data collection because it meant my research wouldn’t accurately represent how skeptical users are of fake news on social media. After receiving feedback from my classmates, I decided to go a different route. I emailed a professor of political science from a local community college, which was a class I’d taken previously, and asked him to distribute the survey amongst his students. I chose to email this professor not only to increase the number of responses of my survey, but also because his students were more politically knowledgeable, which fits the theme of my research. Because of this more specific demographic, I gathered deeper insights which ultimately increased the reliability and credibility of my research. After about a month of survey distribution, I closed it with 81 total respondents to analyze. 3.2.2. Data Analysis For my data analysis, I used comparative analysis to see the effectiveness of my warning sign in detecting political fake news. To do so, I compared the responses to each social media post I presented. I primarily looked at the 4 fake posts, with two of them having warning signs. I then looked for how well the participants could detect fake news on the fake posts without the warning sign and with the warning sign, allowing me to draw conclusions about the effectiveness of my warning sign. Additionally, I looked at the real social media posts to find any variables that could explain what made some participants vote for them being fake. This made me think about verification symbols on social media platforms, which may play a big role in influencing people to believe in a source, even if it’s fake. Looking at the demographics section of my survey, I compared how variables such as age, gender, education, and political beliefs, related to how well the participants from varying backgrounds were able to detect fake news. I did so by looking at the amount of users which were a certain demographic, and compared it to how many users believed or didn’t believe whether something was fake. By analyzing these conditions, I drew trends, patterns, and conclusions about the different factors which influenced the participants’ ability to detect fake news. 4. Results 4.1. Research Question Originally, my research question was worded to ask, “How does the spread of fake news and misinformation through social media affect societal voter behavior in presidential elections within the United States, and how can this be mitigated?” However, this research question was too disconnected and consisted of two completely different questions which my study wasn’t able to answer. Instead, I decided to revise my research question to say “What can be done to mitigate the spread of political fake news on social media, thereby reducing its impact on voting behavior during presidential elections within the United States?” By illustrating my research question into a problem-solution format, this revision allowed me to search for a potential solution to combat political fake news, instead of answering unnecessary questions which were already addressed in the previous literature. 4.1.1. Claim 1: The Warning Sign Is Effective to Possibly Combat Political Fake News Note. This was the 1st post presented to the participants in the fake news experiment section, which showed a fake social media post with a warning sign. Figure 1 Participants Detecting Political Fake News World Journal of Advanced Research and Reviews, 2025, 27(03), 1285-1293 1290 Note. This was the 2nd post presented to the participants in the fake news experiment which showed a fake social media post with no warning sign. Figure 2 Participants detecting political fake news Figure 1 and Figure 2 present the findings of fake social media posts which were shown to the participants in the study. Using the Likert scale, the participants ranked from a scale of 1 to 5 of how fake or real they thought it was, with 1 being that the participants are very confident that the post is fake 5 being that they’re very confident it’s real. A difference between these two posts was that Figure 1 utilized the warning sign, whereas Figure 2 did not. When looking at the results, there is a clear demonstration that participants were able to detect political fake news better in Figure 1 than in Figure 2, with the majority of respondents voting 1-3 in Figure 1, and 3-5 in Figure 2. This means that the warning sign was effective in being a possible factor to help the participants detect fake news. Although the warning sign may not have been the only factor, it’s still an important variable to consider because it shows a measurable difference in how the participants perceived the post. 4.1.2. Claim 2: The Verification Symbol can be Dangerous Note. This was the 5th post presented to the participants in the fake news experiment section, which showed a verification symbol. Figure 3 Participants detecting political fake news World Journal of Advanced Research and Reviews, 2025, 27(03), 1285-1293 1291 Note. This was the 5th social media post presented to the participants, which was fake and included a verification symbol from Twitter. Figure 4 Social Media Post about Barack Obama and Kamala Harris Figure 3 presents the findings of Figure 4, which was a fake social media post about Barack Obama talking harmfully about Kamala Harris. Unlike some of the others, the post didn’t include a warning sign; it included a verification symbol which was given to the account by Twitter. This is important because when looking at the results, there is a dispersed number of votes for the post being fake or real, with the majority of participants voting for the neutral option and somewhat evenly for 1-2 (Fake) and 4-5 (Real). This means that the majority of the participants were unsure of the social media post being fake or real. Although the absence of the warning sign may have played a role in this—which strengthens my first claim—it’s important to account for the verification symbol shown in the post. Verification symbols are a common feature for accounts across social media, and they can influence users to believe certain information. Although social media users may feel inclined to believe news being spread by a verified account, this can still cause users to fall for fake news because verified accounts still have the ability to spread fake information. This finding suggests that verification symbols can potentially create a false sense of trust between users, leading them to be less skeptical of fake news. Although this may not be the only factor for users to determine whether something is fake or not, it’s important to hypothesize the verification symbol may be a possible factor which affects users’ susceptibility to fake news. Limitations Before discussing, it’s important to understand the limitations of the study which could have hindered results. First, the sample size of my study was small. As compared to Quétier-Parent et al. (2023), which included 8000 participants, my sample size of 81 participants wasn’t enough to provide the most accurate results about social media users. However, this doesn’t take away the fact that the findings weren’t significant, since the study proved how the warning sign was effective. Additionally, having only 81 participants limited my ability to accurately find trends across different demographics. While I was still able to see variables in how the participants perceived the news, the results would have been more accurate if tested with a much larger sample size. So although my hypothesis was correct about the warning sign being effective, it would be beneficial to experiment with a broader group. Another limitation I faced was the environment of my study not being on social media, which could have skewed the results. My study did present social media posts to the participants; however, this didn’t capture the accurate environment for which a user scrolls on social media. Since the participants knew they would be answering questions about information they viewed on social media, this could’ve caused them to be more skeptical of what they were reading, whereas in the normal social media environment they wouldn’t have. Loos and Nijenhuis (2020, p. 69-88) were able to avoid this in their study since their survey was conducted on Facebook itself. This approach provided a more World Journal of Advanced Research and Reviews, 2025, 27(03), 1285-1293 1292 realistic setting, but also ended up leaving a majority of participants harmed by the fake news they saw. Instead of conducting an experiment on Facebook or google forms, perhaps there is a better solution to counter this limitation to ensure no results are skewed. Another limitation of my study was how the warning sign may not have been the only factor which allowed the participants to be skeptical of fake news. Since all participants viewed the same social media posts, this limited the ability to see the specific impact of the warning sign. Perhaps conducting a study similar to Ross et al. (2018) would have been more effective, where I present different groups of participants with the same post, but one with a warning sign and one without. This would allow me to ensure that the warning sign was a factor allowing participants to combat fake news. 5. Discussion With the information that the warning sign was now effective to combat political fake news, it’s important to look at what this means and why it’s significant. When looking back at the previous literature, Ross et al. (2023) found how both their warning signs and Facebook’s warning sign had limited success in helping people combat fake news. My study adds to this scholarly conversation by providing a warning sign which was effective to combat fake news, unlike Ross et al. (2023) who weren’t able to. From the findings, the participants demonstrated their success in determining which posts were fake and real with the warning sign. This highlights the importance of not just warning users which Facebook does, but actually providing them with the necessary tools and information to combat political fake news. The warning sign tested in this study, since proven effective, has the potential to help social media users become more skeptical of what they see. Research by Allcott and Gentzkow (2017) explained how the influence of fake news on social media users is spread through multiple platforms, but primarily through Facebook and Twitter. With the findings, the warning sign from this study can be utilized on these platforms and others to ensure users are equipped with the necessary tools. This finding highlights the crucial role of warning signs in mitigating the spread of fake news, which could reduce its implications on voting behavior and provide a fairer election. Research by Grinberg et al. (2019) and Budak (2019) also highlight the influence of fake news on social media, and how it influences voter behavior. My findings add onto this by demonstrating how verification symbols can escalate this influence and put users in more danger. Unlike Grinberg et al. (2019), who focused specifically on how fake news shaped peoples’ opinions and not any countermeasures, my findings suggest accounts with a verification symbol can further influence users, as participants can feel more inclined to news given by a verified account because of its authenticity on the app. This can be harmful for users, so a change in how the verification symbol is given and what someone is allowed to upload should be enforced, thus reducing the implications it has on voting behavior. Although this may not be the only factor in the users’ susceptibility to fake news, it’s crucial for social media platforms to provide effective tools to reduce the spread of misinformation. This is significantly beneficial for social media users since political fake news can heavily influence who one votes for. Further exploration into the topic of verification symbols can provide more clarity on how this factor’s implications on social media users can be mitigated, thus providing a fairer election. 6. Conclusions Building upon the findings, this study has significant implications for social media users engaging with fake news. The community of practice, which includes social media users, platforms, policymakers, and researchers, can utilize the findings of this study. Social media users will be benefited with the implementation of the warning sign as it keeps their beliefs of presidents accurate to their knowledge and not fake information. Social media platforms, in coordination with third-party fact checkers, can implement the warning sign the same way they implement their current warning sign, but with some tweaks necessary to place the warning sign on each post, and not just a message. Policymakers can also work with social media workers to understand which content is flagged and how it could be possibly removed. And finally, researchers can test different designs of the warning sign to see which is most effective, ultimately reducing the influence fake news has on voting behavior. Although this study was specifically focused on political fake news, it’s important to go beyond this. I propose to future researchers to explore how the implications of fake news in general can be mitigated, and not only the political aspect. This means that not only will politically fake news be mitigated, but also fake news as a whole. Additionally, although the findings of this study were accurate, I also propose for researchers to conduct studies with a bigger population. A larger sample size would show a more accurate representation of how effective the warning sign is, and potentially making tweaks on the warning sign could further prove its effectiveness. I also propose for researchers to look into the topic of verification symbols, and explore how much or little effect they have on the users’ sense of fake news. Although World Journal of Advanced Research and Reviews, 2025, 27(03), 1285-1293 1293 I couldn’t entirely test this in the study, it’s still a notable factor which could be tested to determine their role and contribution to fake news. These proposals can help advance the understanding of fake news, and better equip social media users to critically evaluate the information they find. 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