Physical Sciences and Mathematics Commons^™

How Museum Utilize Social Media On Communication, Jiake Han

School of Professional Studies

With the development of Internet, social media became more and more popular among people. Many industries realize the importance of social media in business. Traditionally, museum concentrates more on personal visual experience, which is hard to be replaced by online media. However, museums now also put more concentrate on social media platform because it expands the way of engagement. Especially, for Coronavirus, many organizations including museums have to close. Therefore, museums have to depend more on social media platforms to communicate with audiences. This research aims at finding how different kind of social media help museum communicate and engage with …

Social Media Analytics: A Case Study Of Singapore General Election 2020, Sebastian Zhi Tao Khoo, Leong Hock Ho, Ee Hong Lee, Danston Kheng Boon Goh, Zehao Zhang, Swee Hong Ng, Haodi Qi, Kyong Jin Shim

Research Collection School Of Computing and Information Systems

The 2020 Singaporean General Election (GE2020) was a general election held in Singapore on July 10, 2020. In this study, we present an analysis on social conversations about GE2020 during the election period. We analyzed social conversations from popular platforms such as Twitter, HardwareZone, and TR Emeritus.

Digital Social Listening On Conversations About Sexual Harassment, Xuesi Sim, Ern Rae Chang, Yu Xiang Ong, Jie Ying Yeo, Christine Bai Shuang Yan, Eugene Wen Jia Choy, Kyong Jin Shim

Research Collection School Of Computing and Information Systems

In light of the #MeToo movement and publicized sexual harassment incidents in Singapore in recent years, we built an analytics pipeline for performing digital social listening on conversations about sexual harassment for AWARE (Association of Women for Action and Research). Our social network analysis results identified key influencers that AWARE can engage for sexual harassment awareness campaigns. Further, our analysis results suggest new hashtags that AWARE can use to run social media campaigns and achieve greater reach.

Leveraging Profanity For Insincere Content Detection: A Neural Network Approach, Swapna Gottipati, Annabel Tan, David Jing Shan Chow, Joel Wee Kiat Lim

Research Collection School Of Computing and Information Systems

Community driven social media sites are rich sources of knowledge and entertainment and at the same vulnerable to the flames or toxic content that can be dangerous to various users of these platforms as well as to the society. Therefore, it is crucial to identify and remove such content to have a better and safe online experience. Manually eliminating flames is tedious and hence many research works focus on machine learning or deep learning models for automated methods. In this paper, we primarily focus on detecting the insincere content using neural network-based learning methods. We also integrated the profanity features …

Misogyny Detection In Social Media On The Twitter Platform, Elena Shushkevich

Doctoral

The thesis is devoted to the problem of misogyny detection in social media. In the work we analyse the difference between all offensive language and misogyny language in social media, and review the best existing approaches to detect offensive and misogynistic language, which are based on classical machine learning and neural networks. We also review recent shared tasks aimed to detect misogyny in social media, several of which we have participated in. We propose an approach to the detection and classification of misogyny in texts, based on the construction of an ensemble of models of classical machine learning: Logistic Regression, …

An Attention-Based Rumor Detection Model With Tree-Structured Recursive Neural Networks, Jing Ma, Wei Gao, Shafiq Joty, Kam-Fai Wong

Research Collection School Of Computing and Information Systems

Rumor spread in social media severely jeopardizes the credibility of online content. Thus, automatic debunking of rumors is of great importance to keep social media a healthy environment. While facing a dubious claim, people often dispute its truthfulness sporadically in their posts containing various cues, which can form useful evidence with long-distance dependencies. In this work, we propose to learn discriminative features from microblog posts by following their non-sequential propagation structure and generate more powerful representations for identifying rumors. For modeling non-sequential structure, we first represent the diffusion of microblog posts with propagation trees, which provide valuable clues on how …

Applications Of Artificial Intelligence And Graphy Theory To Cyberbullying, Jesse D. Simpson

MSU Graduate Theses

Cyberbullying is an ongoing and devastating issue in today's online social media. Abusive users engage in cyber-harassment by utilizing social media to send posts, private messages, tweets, or pictures to innocent social media users. Detecting and preventing cases of cyberbullying is crucial. In this work, I analyze multiple machine learning, deep learning, and graph analysis algorithms and explore their applicability and performance in pursuit of a robust system for detecting cyberbullying. First, I evaluate the performance of the machine learning algorithms Support Vector Machine, Naïve Bayes, Random Forest, Decision Tree, and Logistic Regression. This yielded positive results and obtained upwards …

Information Retrieval Of Opioid Dependence Medications Reviews From Health-Related Social Media, Seyedeh Samaneh Omranian

Theses and Dissertations

Social media provides a convenient platform for patients to share their drug usage experience with others; consequently, health researchers can leverage this potential data to gain valuable information about users’ drug satisfaction. Since the 1990s, opioid drug abuse has become a national crisis. In order to reduce the dependency of opioids, several drugs have been presented to the market, but little is known about patient satisfaction with these treatments. Sentiment analysis is a method to measure and interpret patients’ satisfaction. In the first phase of this study, we aimed to utilize social media posts to predict patients’ sentiment towards opioid …

Bootstrapping Web Archive Collections From Micro-Collections In Social Media, Alexander C. Nwala

Computer Science Theses & Dissertations

In a Web plagued by disappearing resources, Web archive collections provide a valuable means of preserving Web resources important to the study of past events. These archived collections start with seed URIs (Uniform Resource Identifiers) hand-selected by curators. Curators produce high quality seeds by removing non-relevant URIs and adding URIs from credible and authoritative sources, but this ability comes at a cost: it is time consuming to collect these seeds. The result of this is a shortage of curators, a lack of Web archive collections for various important news events, and a need for an automatic system for generating seeds. …

Patterns Of Population Displacement During Mega-Fires In California Detected Using Facebook Disaster Maps, Shenyue Jia, Seung Hee Kim, Son V. Nghiem, Paul Doherty, Menas Kafatos

Mathematics, Physics, and Computer Science Faculty Articles and Research

The Facebook Disaster Maps (FBDM) work presented here is the first time this platform has been used to provide analysis-ready population change products derived from crowdsourced data targeting disaster relief practices. We evaluate the representativeness of FBDM data using the Mann-Kendall test and emerging hot and cold spots in an anomaly analysis to reveal the trend, magnitude, and agglommeration of population displacement during the Mendocino Complex and Woolsey fires in California, USA. Our results show that the distribution of FBDM pre-crisis users fits well with the total population from different sources. Due to usage habits, the elder population is underrepresented …

Real-Time Tracking And Mining Of Users’ Actions Over Social Media, Ejub Kajan, Noura Faci, Zakaria Maamar, Mohamed Sellami, Emir Ugljanin, Hamamache Kheddouci, Dragan H. Stojanović, Djamal Benslimane

All Works

© 2020, ComSIS Consortium. All rights reserved. With the advent of Web 2.0 technologies and social media, companies are actively looking for ways to know and understand what users think and say about their products and services. Indeed, it has become the practice that users go online using social media like Facebook to raise concerns, make comments, and share recommendations. All these actions can be tracked in real-time and then mined using advanced techniques like data analytics and sentiment analysis. This paper discusses such tracking and mining through a system called Social Miner that allows companies to make decisions about …

Young People, Social Media, And Impacts On Well-Being, Andreana Nop

School of Professional Studies

Millennials and Generation Z were born into an age where social media and digital technology have been integrated in nearly all aspects of their lives. While social media has proven to be a valuable communication tool in connecting with each other and sharing information, the long-term psychosocial effects are beginning to become more apparent as social media matures. This study analyzes what these effects are and how communication is impacted for these young people. It questions how young people can leverage social media and decrease harm. The study will be conducted through a literature review and analysis. Its goal is …

Understanding Personal Data In The World Of Social Media, Nicholas Scott Rodgers

Undergraduate Honors Capstone Projects

Personal data is behind many of the online interactions that people have through social media and other online sites and services. This data allows sites to understand their users, which in turn allows them to provide better content for their users. This data is also used to determine user interests, which these online services use to target more relevant advertising to their users, and share the information that they collect about their users with third parties. It is only recently that this personal data is being regulated by lawmakers, the businesses running these sites are held accountable for managing the …

Detecting Fake News In Social Media: An Asia-Pacific Perspective, Meeyoung Cha, Wei Gao, Cheng-Te Li

Research Collection School Of Computing and Information Systems

In March 2011, the catastrophic accident known as "The Fukushima Daiichi nuclear disaster" took place, initiated by the Tohoku earthquake and tsunami in Japan. The only nuclear accident to receive a Level-7 classification on the International Nuclear Event Scale since the Chernobyl nuclear power plant disaster in 1986, the Fukushima event triggered global concerns and rumors regarding radiation leaks. Among the false rumors was an image, which had been described as a map of radioactive discharge emanating into the Pacific Ocean, as illustrated in the accompanying figure. In fact, this figure, depicting the wave height of the tsunami that followed, …

Interpretable Rumor Detection In Microblogs By Attending To User Interactions, Ling Min Serena Khoo, Hai Leong Chieu, Zhong Qian, Jing Jiang

Research Collection School Of Computing and Information Systems

We address rumor detection by learning to differentiate between the community’s response to real and fake claims in microblogs. Existing state-of-the-art models are based on tree models that model conversational trees. However, in social media, a user posting a reply might be replying to the entire thread rather than to a specific user. We propose a post-level attention model (PLAN) to model long distance interactions between tweets with the multi-head attention mechanism in a transformer network. We investigated variants of this model: (1) a structure aware self-attention model (StA-PLAN) that incorporates tree structure information in the transformer network, and (2) …

Multi-Class Twitter Data Categorization And Geocoding With A Novel Computing Framework, Sakib Mahmud Khan, Mashrur Chowdhury, Linh B. Ngo, Amy Apon

Computer Science Faculty Publications

This study details the progress in transportation data analysis with a novel computing framework in keeping with the continuous evolution of the computing technology. The computing framework combines the Labeled Latent Dirichlet Allocation (L-LDA)-incorporated Support Vector Machine (SVM) classifier with the supporting computing strategy on publicly available Twitter data in determining transportation-related events to provide reliable information to travelers. The analytical approach includes analyzing tweets using text classification and geocoding locations based on string similarity. A case study conducted for the New York City and its surrounding areas demonstrates the feasibility of the analytical approach. Approximately 700,010 tweets are analyzed …

Ethics, Privacy And Data Collection: A Complex Intersection, Matthew S. Brown

Honors Theses

The technology around us enables incredible abilities such as high-resolution video calls and the ability to stay connected with everyone we care about through social media. This technology also comes with a hidden cost in the form of data collection.

This work explores what privacy means and how users understand what data social media companies collect and monetize. This thesis also proposes a more ethical business model that addresses privacy concerns from an individual perspective.

Development Of Machine Learning Models To Predict The Online Impact Of Research, Mohammed Murtuza Shahzad Syed

Graduate Research Theses & Dissertations

Scientific research is being increasingly shared online in a way such that there is a need to develop methodologies to measure the impact of specific papers in ways that go beyond traditional indicators of scholarly citations and beyond the scholarly community. In this thesis, new machine learning models are developed to measure and predict the impact ofresearch in the online context. The extent to which research papers are mentioned on social media platforms, i.e., their online sustainability, indicates the public's interest in and perhaps even the level of understanding of scientific topics. A research paper having a long lifespan, i.e., …

Health Risks Of E-Cigarettes: Analysis Of Twitter Data Using Topic Mining, Abdullah Wahbeh, Mohammad A. Al-Ramahi, Omar El-Gayar, Tareq Nasralah

Computer Information Systems Faculty Publications

The recent rise of e-cigarettes and vaping products has increased concerns that another young generation may become addicted to nicotine. Recently, it becomes evident that several health issues are related to the use of e-cigarettes and vaping products. The objective of this paper is to understand and identify such health issues by collecting and analyzing social media data. The analysis reflects the most important themes and topics discussed by online user’s about e-cigarettes, vaping, and associated health issues. Using topic modeling techniques, we were able to identify several health issues related to the use of e-cigarettes and vaping products. These …

The Emotions Of Science: Using Social Media To Gauge Public Emotions Toward Research Topics, Cole C. Freeman

Graduate Research Theses & Dissertations

Online and in the real world, communities are bonded together by emotional consensus around core issues. Emotional responses to scientific findings often play a pivotal role in these core issues. When there is too much diversity of opinion on topics of science, emotions flare up and give rise to conflict. This conflict threatens positive outcomes for research. Emotions have the power to shape how people process new information. They can color the public's understanding of science, motivate policy positions, even change lives. And yet little work has been done to evaluate the public's emotional response to science using quantitative methods. …

The Social Media Machines: An Investigation Of The Effect Of Trust Moderated By Disinformation On Users’ Decision-Making Process, Zulma Valedon Westney

CCE Theses and Dissertations

Social media networking sites (SMNS) have become a popular communications medium where users share information, knowledge, and persuasion. In less than two decades, social media's (SM) dominance as a communication medium can't be disputed, for good or evil. Combined with the newly found immediacy and pervasiveness, these SM applications' persuasive power are useful weapons for organizations, angry customers, employees, actors, and activists bent on attacking or hacking other individuals, institutions, or systems. Consequently, SM has become the preferred default mechanism of news sources; however, users are unsure if the information gathered is true or false. According to the literature, SMNS …

Social Media Based Algorithmic Clinical Decision Support Learning From Behavioral Predispositions, Radhika V. Medury

Doctoral Dissertations

Behavioral disorders are disabilities characterized by an individual’s mood, thinking, and social interactions. The commonality of behavioral disorders amongst the United States population has increased in the last few years, with an estimated 50% of all Americans diagnosed with a behavioral disorder at some point in their lifetime. AttentionDeficit/Hyperactivity Disorder is one such behavioral disorder that is a severe public health concern because of its high prevalence, incurable nature, significant impact on domestic life, and peer relationships. Symptomatically, in theory, ADHD is characterized by inattention, hyperactivity, and impulsivity. Access to providers who can offer diagnosis and treat the disorder varies …

Optimal Feature Selection For Learning-Based Algorithms For Sentiment Classification, Zhaoxia Wang, Zhiping Lin

Research Collection School Of Computing and Information Systems

Sentiment classification is an important branch of cognitive computation—thus the further studies of properties of sentiment analysis is important. Sentiment classification on text data has been an active topic for the last two decades and learning-based methods are very popular and widely used in various applications. For learning-based methods, a lot of enhanced technical strategies have been used to improve the performance of the methods. Feature selection is one of these strategies and it has been studied by many researchers. However, an existing unsolved difficult problem is the choice of a suitable number of features for obtaining the best sentiment …