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The way media portray public health problems influences the public’s perception of problems and related solutions. Social media allows users to engage with news and to collectively construct meaning. This paper examined news in comparison to user-generated content related to opioids to understand the role of second-level agenda-setting in public health. We analyzed 162,760 tweets about the opioid crisis, and compared the main topics and their sentiments with 2998 opioid stories from The New York Times online. Evidence from this study suggests that second-level agenda setting on social media is different from the news; public communication about opioids on X/Twitter highlights attributes that are different from those highlighted in the news. The findings suggest that public health communication should strategically utilize social media data, including obtaining consumer insight from personal tweets, listening to diverse views and warning signs from issue tweets, and tuning in to the media for policy trends.
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Fake Tweets Detection and Its Impacts on the 2020 U.S. Election Prediction
The increased social media usage in modern history instigates data collection from various users with different backgrounds. Mass media has been a rich source of information and might be utilized for countless purposes, from business and personal to political determination. Because more people tend to express their opinions through social media platforms, researchers are excited to collect data and use it as a free survey tool on what the public ponders about a particular issue. Because of the detrimental effect of news on social networks, many irresponsible users generate and promote fake news to influence public belief on a specific issue. The U.S. presidential election has been a significant and popular event, so both parties invest and extend their efforts to pursue and win the general election. Undoubtedly, spreading and promoting fake news through social media is one of the ways negligent individuals or groups sway societies toward their goals. This project examined the impact of removing fake tweets to predict the electoral outcomes during the 2020 general election. Eliminating mock tweets has improved the correctness of model prediction from 74.51 percent to 86.27 percent with the electoral outcomes of the election. Finally, we compared classification model performances with the highest model accuracy of 99.74634 percent, precision of 99.99881 percent, recall of 99.49430 percent, and an F1 score of 99.74592 percent. The study concludes that removing fake tweets improves the correctness of the model with the electoral outcomes of the U.S. election.
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- Award ID(s):
- 2131269
- PAR ID:
- 10546095
- Publisher / Repository:
- IEEE
- Date Published:
- Subject(s) / Keyword(s):
- fake tweets detection machine learning election prediction social media
- Format(s):
- Medium: X
- Location:
- Washington, DC
- Sponsoring Org:
- National Science Foundation
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