Analysis of Information Diffusion through Social Media

dc.contributor.authorKhalili, Nastaranen
dc.contributor.committeechairGhaffarzadegan, Naviden
dc.contributor.committeememberAndalib, Maryam Alsadaten
dc.contributor.committeememberKong, Zhenyuen
dc.contributor.committeememberJardine, Ericen
dc.contributor.departmentIndustrial and Systems Engineeringen
dc.description.abstractThe changes in the course of communication changed the world from different perspectives. Public participation on social media means the generation, diffusion, and exposure to a tremendous amount of user-generated content without supervision. This four-essay dissertation analyzes information diffusion through social media and its opportunities and challenges through management systems engineering and data analytics. First, we evaluate how information can be shared to reach maximum exposure for the case on online petitions. We use system dynamics modeling and propose policies for campaign managers to schedule the reminders they send to have the highest number of petition signatures. We find that sending reminders is more effective in the case of increasing the signature rate. In the second essay, we investigate how people build trust/ mistrust in science during an emergency. We use data analytics methods on more than 700,000 tweets containing keywords of Hydroxychloroquine and chloroquine, two candidate medicines, to prevent and cure patients infected with COVID-19. We show that people's opinions are concentrated in the case of polarity and spread out in the case of subjectivity. Also, they tend to share subjective tweets than objective ones. In the third essay, building on the same dataset as essay two, we study the changes in science communication during the coronavirus pandemic. We used topic modeling and clustered the tweets into seven different groups. Our analysis suggests that a highly scientific and health-related subject can become political in the case of an emergency. We found that the groups of medical information and research and study have fewer tweets than the political one. Fourth, we investigated fake news diffusion as one of the main challenges of user-generated content. We built a system dynamics model and analyzed the effects of competition and correction in combating fake news. We show that correction of misinformation and competition in fake news needs a high percentage of participation to be effective enough to deal with fake news.en
dc.description.abstractgeneralThe prevalence of social media among people has changed information diffusion in several ways. This change caused the emergence of a variety of opportunities and challenges. We discuss instances of these in this dissertation in four main essays. In the first essay, we study online social and political campaigns. Considering the main goal of campaign managers is to gain the highest reach and signatures, we generate a model to show the effects of sending reminders after the initial announcement and its schedule on the final total number of signatures. We found that the best policy for online petition success is sending reminders when people are increasingly signing it rather than when people lose interest in it. In the second essay, we investigated how people build trust/ mistrust in scientific information in emergency cases. We used public tweets about two candidate medicines to prevent and treat patients infected with COVID-19 and analyzed them. Our results suggest that people trust and retweet the information that is based on emotions and judgments more than the one containing facts. We evaluated the science communication during the mentioned emergency by further investigating the same dataset in the third essay. We clustered all the tweets based on the words they used into seven different groups and labeled each of them. Then, we focused on three groups of medical, research and study, and political. Our analysis suggests that although the subject is a health-related scientific one, the number of tweets in the political group is greater than the other clusters. In the fourth essay, we analyzed the fake news diffusion through social media and the effects of correction and competition on it. In this context, correction means the reaction to misinformation that states its falsity or provides counter facts based on truth. We created a model and simulated it for the competition considering novelty as one influential factor of sharing. The results of this study reveal that active participation in correction and competition is needed to combat fake news effectively.en
dc.description.degreeDoctor of Philosophyen
dc.publisherVirginia Techen
dc.rightsIn Copyrighten
dc.subjectSocial Mediaen
dc.subjectSystem Dynamicsen
dc.subjectData Analyticsen
dc.subjectNatural Language Processingen
dc.titleAnalysis of Information Diffusion through Social Mediaen
dc.typeDissertationen and Systems Engineeringen Polytechnic Institute and State Universityen of Philosophyen


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