Combatting misinformation using foundational biology

Following the onset of the Covid-19 pandemic, a new wave of scientific misinformation flooded the media. Conspiratorial ideas about the virus itself, vaccinations, and a myriad of other things spread through social media like wildfire. These, compounded with preexisting forms of pseudoscientific rhetoric, has led to a dramatic increase in public distrust of science. This type of distrust not only threatens scientific progress, but also holds broader threats to things like public health and policy decisions. Though there are several ways to combat scientific misinformation, this project aimed to cull misinformation by educating the public about the foundational biology underlying many of these conspiracies. Five videos were produced covering mRNA vaccines, alternative medicine, misuse of science to justify racism, and climate change denial. These videos will hopefully provide a more complete context for some conspiracies and aid the audience in developing skills to recognize and combat misinformation as they encounter it.Thesis (B.?)Honors Colleg

Ebola the Enemy: How the U.S. Media Militarized the 2014 Ebola Epidemic

The 2014 Ebola outbreak shocked the world. In western Africa, the scale of the tragedy was surprising. But equally surprising was the excessively fearful response of the international public to a disease that most public health experts agreed was unlikely to significantly impact countries with strong healthcare infrastructures. This included the United States, where the intensity of fear with which the American public responded was disproportionate to the actual threat. Because the outbreak is still recent, most research into America’s response to Ebola has focused on trying to characterize or quantify the extreme reaction that the epidemic produced, with only speculation as to what caused the fear. This paper will demonstrate that the public’s fear of Ebola had at least one specific cause: the distinctly militarized language that the media used to describe the disease. Because of the media’s use of military terms, the American people were inclined to view Ebola more as a military enemy than a medical one, and they largely reacted with three responses associated with the threat of war: fear, isolationism, and aggression. The public became reluctant to send the aid to Africa that many public health officials agreed was necessary to stop the epidemic. This paper argues that the media’s irresponsible use of military language when discussing the epidemic helped cause the unhelpful mass panic among American citizens, when a humanitarian response characterized by increased aid to the affected countries would have been more effective in controlling the Ebola outbreak and keeping America safe

Rise in Vaccine Distrust as a Result of the Covid-19 Pandemic

Since the Covid-19 pandemic began in 2020, there has been a decrease in vaccine administration due to the increased spread of misinformation and disinformation. This is a result of distrust in healthcare following the global pandemic. The spread of misinformation and disinformation in social media about the Covid-19 vaccine has caused parents to question routine childhood vaccines as well. This has increased the cases of viral outbreaks that could be prevented by vaccination and lead to reemergence of previously tamed viruses and an ultimate downfall in global health. These risks are preventable by spreading awareness of the problem of the spreading of false information. Solutions include encouraging individuals to research and inform themselves about vaccines rather than trusting information they see on social media, encouraging those who have questions to ask their doctors for further information, and using reliable, peer reviewed resources when collecting information

Diffusion of Falsehoods on Social Media

Misinformation has captured the interest of academia in recent years with several studies looking at the topic broadly. However, these studies mostly focused on rumors which are social in nature and can be either classified as false or real. In this research, we attempt to bridge the gap in the literature by examining the impacts of user characteristics and feature contents on the diffusion of (mis)information using verified true and false information. We apply a topic allocation model augmented by both supervised and unsupervised machine learning algorithms to identify tweets on novel topics. We find that retweet count is higher for fake news, novel tweets, and tweets with negative sentiment and lower lexical structure. In addition, our results show that the impacts of sentiment are opposite for fake news versus real news. We also find that tweets on the environment have a lower retweet count than the baseline religious news and real social news tweets are shared more often than fake social news. Furthermore, our studies show the counter intuitive nature of current correction endeavors by FEMA and other fact checking organizations in combating falsehoods. Specifically, we show that even though fake news causes an increase in correction messages, they influenced the propagation of falsehoods. Finally our empirical results reveal that correction messages, positive tweets and emotionally charged tweets morph faster. Furthermore, we show that tweets with positive sentiment or are emotionally charged morph faster over time. Word count and past morphing history also positively affect morphing behavior

FAKE NEWS DETECTION ON THE WEB: A DEEP LEARNING BASED APPROACH

The acceptance and popularity of social media platforms for the dispersion and proliferation of news articles have led to the spread of questionable and untrusted information (in part) due to the ease by which misleading content can be created and shared among the communities. While prior research has attempted to automatically classify news articles and tweets as credible and non-credible. This work complements such research by proposing an approach that utilizes the amalgamation of Natural Language Processing (NLP), and Deep Learning techniques such as Long Short-Term Memory (LSTM). Moreover, in Information System’s paradigm, design science research methodology (DSRM) has become the major stream that focuses on building and evaluating an artifact to solve emerging problems. Hence, DSRM can accommodate deep learning-based models with the availability of adequate datasets. Two publicly available datasets that contain labeled news articles and tweets have been used to validate the proposed model’s effectiveness. This work presents two distinct experiments, and the results demonstrate that the proposed model works well for both long sequence news articles and short-sequence texts such as tweets. Finally, the findings suggest that the sentiments, tagging, linguistics, syntactic, and text embeddings are the features that have the potential to foster fake news detection through training the proposed model on various dimensionality to learn the contextual meaning of the news content

A Computational Model and Convergence Theorem for Rumor Dissemination in Social Networks

The spread of rumors, which are known as unverified statements of uncertain origin, may cause tremendous number of social problems. If it would be possible to identify factors affecting spreading a rumor (such as agents' desires, trust network, etc.), then this could be used to slowdown or stop its spreading. A computational model that includes rumor features and the way a rumor is spread among society's members, based on their desires, is therefore needed. Our research is centering on the relation between the homogeneity of the society and rumor convergence in it and result shows that the homogeneity of the society is a necessary condition for convergence of the spreading rumor.Comment: 29 pages, 7 figure

The Impact of Sentiment and Misinformation Cycling Through the Social Media Platform, Twitter, During the Initial Phase of the COVID-19 Vaccine Rollout

This study assesses the underlying topics, sentiment, and types of information regarding COVID-19 vaccines on Twitter during the initiation of the vaccine rollout. Tweets about the COVID-19 vaccine were collected and the relevant tweets were then filtered out using a relevancy classifier. Latent Dirichlet Allocation (LDA) was used to uncover topics of discussion within the relevant tweets. The NRC lexicon was used to assess positive and negative sentiment within tweets. The type of information (information, misinformation, opinion, or question) in tweets was evaluated. The relevancy classifier resulted in a dataset of 210,657 relevant tweets. Eight topics provided the best representation of the relevant tweets. Tweets with negative sentiment were associated with a higher percentage of misinformation. Tweets with positive sentiment showed a higher percentage of information. The proliferation of information and misinformation on social media platforms are associated with building trust and mitigating negative sentiment associated with COVID-19 vaccines