Physical Sciences and Mathematics Commons^™

Early Detection Of Fake News On Social Media, Yang Liu

Dissertations

The ever-increasing popularity and convenience of social media enable the rapid widespread of fake news, which can cause a series of negative impacts both on individuals and society. Early detection of fake news is essential to minimize its social harm. Existing machine learning approaches are incapable of detecting a fake news story soon after it starts to spread, because they require certain amounts of data to reach decent effectiveness which take time to accumulate. To solve this problem, this research first analyzes and finds that, on social media, the user characteristics of fake news spreaders distribute significantly differently from those …

Bullynet: Unmasking Cyberbullies On Social Networks, Aparna Sankaran

Boise State University Theses and Dissertations

Social media has changed the way people communicate with each other, and consecutively affected people's ability to empathize in both positive and negative ways. One of the most harmful consequences of social media is the rise of cyberbullying, which tends to be more sinister than traditional bullying given that online records typically live on the internet for quite a long time and are hard to control. In this thesis, we present a three-phase algorithm, called BullyNet, for detecting cyberbullies on Twitter social network. We exploit bullying tendencies by proposing a robust method for constructing a cyberbullying signed network. BullyNet analyzes …

Social Media Sentiment Analysis With A Deep Neural Network: An Enhanced Approach Using User Behavioral Information, Ahmed Sulaiman M. Alharbi

Dissertations

Sentiment analysis on social media such as Twitter has become a very important and challenging task. Due to the characteristics of such data (including tweet length, spelling errors, abbreviations, and special characters), the sentiment analysis task in such an environment requires a non-traditional approach. Moreover, social media sentiment analysis constitutes a fundamental problem with many interesting applications, such as for Business Intelligence, Medical Monitoring, and National Security. Most current social media sentiment classification methods judge the sentiment polarity primarily according to textual content and neglect other information on these platforms. In this research, we propose deep learning based frameworks that …

Machine Learning-Based Models For Assessing Impacts Before, During And After Hurricane Events, Julie L. Harvey

Electronic Theses and Dissertations

Social media provides an abundant amount of real-time information that can be used before, during, and after extreme weather events. Government officials, emergency managers, and other decision makers can use social media data for decision-making, preparation, and assistance. Machine learning-based models can be used to analyze data collected from social media. Social media data and cloud cover temperature as physical sensor data was analyzed in this study using machine learning techniques. Data was collected from Twitter regarding Hurricane Florence from September 11, 2018 through September 20, 2018 and Hurricane Michael from October 1, 2018 through October 18, 2018. Natural language …

A Pedagogy Of Techno-Social Relationality: Ethics And Digital Multimodality In The Composition Classroom, Kristin M. Ravel

Theses and Dissertations

I bring together the relational ethics of feminist critical theory with approaches of multimodal rhetoric to examine the ethical implications of composing on social media platforms. Most social media platforms are designed to value consumerism, efficiency, quantity of web traffic, and constant synchronous response over concerns of responsible and critical communication. I propose a rhetorical approach of techno-social relationality (TSR) as an intervention against such corporate-minded design. Through this approach, I argue that civil engagement is not limited to people’s social responsibilities but rather is entwined in complex, material-technical contexts. By considering the responsibility of our machines as much as …

A Multimodal Approach To Sarcasm Detection On Social Media, Dipto Das

MSU Graduate Theses

In recent times, a major share of human communication takes place online. The main reason being the ease of communication on social networking sites (SNSs). Due to the variety and large number of users, SNSs have drawn the attention of the computer science (CS) community, particularly the affective computing (also known as emotional AI), information retrieval, natural language processing, and data mining groups. Researchers are trying to make computers understand the nuances of human communication including sentiment and sarcasm. Emotion or sentiment detection requires more insights about the communication than it does for factual information retrieval. Sarcasm detection is particularly …

Worcester Chamber Of Commerce: Recruiting Minority Business Owners, Ryan Dimaria, Alexander Hull, Xikun Lu, Haopeng Wang, Jiacheng Hou, Danning Zhao

School of Professional Studies

Our capstone project was to help the Worcester Regional Chamber of Commerce identify how to re-frame their marketing so it would be appealing to immigrant and minority owned businesses. Based on interviews and external research, our group was able to create a tangible and resourceful data set that provided justified recommendations and ideas on how the Chamber could make adjustments to their marketing plan to attract more businesses of this particular demographic in the city of Worcester. By implementing these recommendations, we believe the Chamber has the opportunity to create a more diverse group of Chamber members, add value to …

Use Of Text Data In Identifying And Prioritizing Potential Drug Repositioning Candidates, Majid Rastegar-Mojarad

Theses and Dissertations

New drug development costs between 500 million and 2 billion dollars and takes 10-15 years, with a success rate of less than 10%. Drug repurposing (defined as discovering new indications for existing drugs) could play a significant role in drug development, especially considering the declining success rates of developing novel drugs. In the period 2007-2009, drug repurposing led to the launching of 30-40% of new drugs. Typically, new indications for existing medications are identified by accident. However, new technologies and a large number of available resources enable the development of systematic approaches to identify and validate drug-repurposing candidates with significantly …

A Case Study On Social Media As An Effective Management Tool, Appolloh Omolloh

Walden Dissertations and Doctoral Studies

In small businesses in the United States, specifically those with fewer than 10 employees,

leaders may be skeptical of and resistant to the use of social media in their management

operations. Management literature does not indicate clear and effective guidelines and

policies detailing small marketing firms use of social media. The purpose of this

qualitative case study was to explore the perceptions of small marketing firm leaders

about the resources and knowledge needed for effective use of social media as a

management tool. Emerson's social exchange theory grounded the study. The study

targeted owners and managers of small marketing firms …

Privacy Preservation In Social Media Environments Using Big Data, Katrina Ward

Doctoral Dissertations

"With the pervasive use of mobile devices, social media, home assistants, and smart devices, the idea of individual privacy is fading. More than ever, the public is giving up personal information in order to take advantage of what is now considered every day conveniences and ignoring the consequences. Even seemingly harmless information is making headlines for its unauthorized use (18). Among this data is user trajectory data which can be described as a user's location information over a time period (6). This data is generated whenever users access their devices to record their location, query the location of a point …

Data-Driven And Knowledge-Based Strategies For Realizing Crowd Wisdom On Social Media, Shreyansh Bhatt

Browse all Theses and Dissertations

The wisdom of the crowd is a well-known example of collective intelligence wherein an aggregated judgment of a group of individuals is superior to that of an individual. The aggregated judgment is surprisingly accurate for predicting the outcome of a range of tasks from geopolitical forecasting to the stock price prediction. Recent research has shown that participants' previous performance data contributes to the identification of a subset of participants that can collectively predict an accurate outcome. In the absence of such performance data, researchers have explored the role of human-perceived diversity, i.e., whether a human considers a crowd as a …