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Wright State University

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A Twitter-Based Study For Understanding Public Reaction On Zika Virus, Roopteja Muppalla Jan 2018

A Twitter-Based Study For Understanding Public Reaction On Zika Virus, Roopteja Muppalla

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In recent times, social media platforms like Twitter have become more popular and people have become more interactive and responsive than before. People often react to every news in real-time and within no-time, the information spreads rapidly. Even with viral diseases like Zika, people tend to share their opinions and concerns on social media. This can be leveraged by the health officials to track the disease in real-time thereby reducing the time lag due to traditional surveys. A faster and accurate detection of the disease can allow health officials to understand people's opinion of the disease and take necessary ...


Content-Based Clustering And Visualization Of Social Media Text Messages, Sydney A. Barnard Jan 2018

Content-Based Clustering And Visualization Of Social Media Text Messages, Sydney A. Barnard

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Although Twitter has been around for more than ten years, crisis management agencies and first response personnel are not able to fully use the information this type of data provides during a crisis or natural disaster. This thesis addresses clustering and visualizing social media data by textual similarity, rather than by only time and location, as a tool for first responders. This thesis presents a tool that automatically clusters geotagged text data based on their content and displays the clusters and their locations on the map. It allows at-a-glance information to be displayed throughout the evolution of a crisis. For ...


A Semantically Enhanced Approach To Identify Depression-Indicative Symptoms Using Twitter Data, Ankita Saxena Jan 2018

A Semantically Enhanced Approach To Identify Depression-Indicative Symptoms Using Twitter Data, Ankita Saxena

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According to the World Health Organization, more than 300 million people suffer from Major Depressive Disorder (MDD) worldwide. PHQ-9 is used to screen and diagnose MDD clinically and identify its severity. With the unprecedented growth and enthusiastic acceptance of social media such as Twitter, a large number of people have come to share their feelings and emotions on it openly. Each tweet can indicate a user's opinion, thought or feeling. A tweet can also indicate multiple symptoms related to PHQ-9. Identifying PHQ-9 symptoms indicated by a tweet can provide crucial information about a user regarding his/her depression diagnosis ...


Harassment Detection On Twitter Using Conversations, Venkatesh Edupuganti Jan 2017

Harassment Detection On Twitter Using Conversations, Venkatesh Edupuganti

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Social media has brought people closer than ever before, but the use of social media has also brought with it a risk of online harassment. Such harassment can have a serious impact on a person such as causing low self-esteem and depression. The past research on detecting harassment on social media is primarily based on the content of messages exchanged on social media. The lack of context when relying on a single social media post can result in a high degree of false alarms. In this study, I focus on the reliable detection of harassment on Twitter by better understanding ...


Personalized And Adaptive Semantic Information Filtering For Social Media, Pavan Kapanipathi Jan 2016

Personalized And Adaptive Semantic Information Filtering For Social Media, Pavan Kapanipathi

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Social media has experienced immense growth in recent times. These platforms are becoming increasingly common for information seeking and consumption, and as part of its growing popularity, information overload pose a significant challenge to users. For instance, Twitter alone generates around 500 million tweets per day and it is impractical for users to have to parse through such an enormous stream to find information that are interesting to them. This situation necessitates efficient personalized filtering mechanisms for users to consume relevant, interesting information from social media. Building a personalized filtering system involves understanding users' interests and utilizing these interests to ...