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- All Subjects: Social Media
Description
Crises or large-scale emergencies such as earthquakes and hurricanes cause massive damage to lives and property. Crisis response is an essential task to mitigate the impact of a crisis. An effective response to a crisis necessitates information gathering and analysis. Traditionally, this process has been restricted to the information collected by first responders on the ground in the affected region or by official agencies such as local governments involved in the response. However, the ubiquity of mobile devices has empowered people to publish information during a crisis through social media, such as the damage reports from a hurricane. Social media has thus emerged as an important channel of information which can be leveraged to improve crisis response. Twitter is a popular medium which has been employed in recent crises. However, it presents new challenges: the data is noisy and uncurated, and it has high volume and high velocity. In this work, I study four key problems in the use of social media for crisis response: effective monitoring and analysis of high volume crisis tweets, detecting crisis events automatically in streaming data, identifying users who can be followed to effectively monitor crisis, and finally understanding user behavior during crisis to detect tweets inside crisis regions. To address these problems I propose two systems which assist disaster responders or analysts to collaboratively collect tweets related to crisis and analyze it using visual analytics to identify interesting regions, topics, and users involved in disaster response. I present a novel approach to detecting crisis events automatically in noisy, high volume Twitter streams. I also investigate and introduce novel methods to tackle information overload through the identification of information leaders in information diffusion who can be followed for efficient crisis monitoring and identification of messages originating from crisis regions using user behavior analysis.
ContributorsKumar, Shamanth (Author) / Liu, Huan (Thesis advisor) / Davulcu, Hasan (Committee member) / Maciejewski, Ross (Committee member) / Agarwal, Nitin (Committee member) / Arizona State University (Publisher)
Created2015
Description
With the rise of social media, hundreds of millions of people spend countless hours all over the globe on social media to connect, interact, share, and create user-generated data. This rich environment provides tremendous opportunities for many different players to easily and effectively reach out to people, interact with them, influence them, or get their opinions. There are two pieces of information that attract most attention on social media sites, including user preferences and interactions. Businesses and organizations use this information to better understand and therefore provide customized services to social media users. This data can be used for different purposes such as, targeted advertisement, product recommendation, or even opinion mining. Social media sites use this information to better serve their users.
Despite the importance of personal information, in many cases people do not reveal this information to the public. Predicting the hidden or missing information is a common response to this challenge. In this thesis, we address the problem of predicting user attributes and future or missing links using an egocentric approach. The current research proposes novel concepts and approaches to better understand social media users in twofold including, a) their attributes, preferences, and interests, and b) their future or missing connections and interactions. More specifically, the contributions of this dissertation are (1) proposing a framework to study social media users through their attributes and link information, (2) proposing a scalable algorithm to predict user preferences; and (3) proposing a novel approach to predict attributes and links with limited information. The proposed algorithms use an egocentric approach to improve the state of the art algorithms in two directions. First by improving the prediction accuracy, and second, by increasing the scalability of the algorithms.
Despite the importance of personal information, in many cases people do not reveal this information to the public. Predicting the hidden or missing information is a common response to this challenge. In this thesis, we address the problem of predicting user attributes and future or missing links using an egocentric approach. The current research proposes novel concepts and approaches to better understand social media users in twofold including, a) their attributes, preferences, and interests, and b) their future or missing connections and interactions. More specifically, the contributions of this dissertation are (1) proposing a framework to study social media users through their attributes and link information, (2) proposing a scalable algorithm to predict user preferences; and (3) proposing a novel approach to predict attributes and links with limited information. The proposed algorithms use an egocentric approach to improve the state of the art algorithms in two directions. First by improving the prediction accuracy, and second, by increasing the scalability of the algorithms.
ContributorsAbbasi, Mohammad Ali, 1975- (Author) / Liu, Huan (Thesis advisor) / Davulcu, Hasan (Committee member) / Ye, Jieping (Committee member) / Agarwal, Nitin (Committee member) / Arizona State University (Publisher)
Created2014
Description
This research start utilizing an efficient sparse inverse covariance matrix (precision matrix) estimation technique to identify a set of highly correlated discriminative perspectives between radical and counter-radical groups. A ranking system has been developed that utilizes ranked perspectives to map Islamic organizations on a set of socio-cultural, political and behavioral scales based on their web site corpus. Simultaneously, a gold standard ranking of these organizations was created through domain experts and compute expert-to-expert agreements and present experimental results comparing the performance of the QUIC based scaling system to another baseline method for organizations. The QUIC based algorithm not only outperforms the baseline methods, but it is also the only system that consistently performs at area expert-level accuracies for all scales. Also, a multi-scale ideological model has been developed and it investigates the correlates of Islamic extremism in Indonesia, Nigeria and UK. This analysis demonstrate that violence does not correlate strongly with broad Muslim theological or sectarian orientations; it shows that religious diversity intolerance is the only consistent and statistically significant ideological correlate of Islamic extremism in these countries, alongside desire for political change in UK and Indonesia, and social change in Nigeria. Next, dynamic issues and communities tracking system based on NMF(Non-negative Matrix Factorization) co-clustering algorithm has been built to better understand the dynamics of virtual communities. The system used between Iran and Saudi Arabia to build and apply a multi-party agent-based model that can demonstrate the role of wedges and spoilers in a complex environment where coalitions are dynamic. Lastly, a visual intelligence platform for tracking the diffusion of online social movements has been developed called LookingGlass to track the geographical footprint, shifting positions and flows of individuals, topics and perspectives between groups. The algorithm utilize large amounts of text collected from a wide variety of organizations’ media outlets to discover their hotly debated topics, and their discriminative perspectives voiced by opposing camps organized into multiple scales. Discriminating perspectives is utilized to classify and map individual Tweeter’s message content to social movements based on the perspectives expressed in their tweets.
ContributorsKim, Nyunsu (Author) / Davulcu, Hasan (Thesis advisor) / Sen, Arunabha (Committee member) / Hsiao, Sharon (Committee member) / Corman, Steven (Committee member) / Arizona State University (Publisher)
Created2018
Description
Long before “fake news” dominated the conversation within and about the media, media literacy advocates have championed the need for media literacy education that provides the tools for people to understand, analyze, and evaluate media messages. That the majority of U.S. adults now consume news on social media underscores the importance for students of all ages to be critical users of media. Furthermore, the affordances of social media to like, comment, and share news items within one’s network increases an individual’s responsibility to ascertain the veracity of news before using a social media megaphone to spread false information. Social media’s shareability can dictate how information spreads, increasing news consumers’ role as a gatekeeper of information and making media literacy education more important than ever.
This research examines the media literacy practices that news consumers use to inform their gatekeeping decisions. Using a constant comparative coding method, the author conducted a qualitative analysis of hundreds of discussion board posts from adult participants in a digital media literacy Massive Open Online Course (MOOC) to identify major themes and examine growth in participants’ sense of responsibility related to sharing news information, their feeling of empowerment to make informed decisions about the media messages they receive, and how the media literacy tools and techniques garnered from the MOOC have affected their daily media interactions. Findings emphasize the personal and contextual nature of media literacy, and that those factors must be addressed to ensure the success of a media literacy education program.
This research examines the media literacy practices that news consumers use to inform their gatekeeping decisions. Using a constant comparative coding method, the author conducted a qualitative analysis of hundreds of discussion board posts from adult participants in a digital media literacy Massive Open Online Course (MOOC) to identify major themes and examine growth in participants’ sense of responsibility related to sharing news information, their feeling of empowerment to make informed decisions about the media messages they receive, and how the media literacy tools and techniques garnered from the MOOC have affected their daily media interactions. Findings emphasize the personal and contextual nature of media literacy, and that those factors must be addressed to ensure the success of a media literacy education program.
ContributorsRoschke, Kristy (Author) / Thornton, Leslie-Jean (Thesis advisor) / Chadha, Monica (Committee member) / Halavais, Alexander (Committee member) / Silcock, Bill (Committee member) / Arizona State University (Publisher)
Created2018
Description
Although previous research has explored the relationship between social media use and well-being, many studies are contradictory of each other and conclude varying findings relating to social media use and outspokenness. This study explores the relationship between active and passive social media use, perceived social media expertise, and outspokenness using the potentially mediating variable of perceived social acceptance. 162 participants, recruited through Amazon Mechanical Turk (MTurk) and ASU’s SONA systems, completed a survey relating to their own use of social media, perceived social acceptance, and outspokenness. Contradictory to my first hypotheses, no significant correlations were found between social media use and social media expertise. However, correlation analyses revealed that active social media use is related to an increased amount of perceived social media expertise (r = 0.23, p < .004). Perceived social media expertise was significantly positively correlated with outspokenness (r = 0.19, p < 0.015); however, it was not correlated with perceived social acceptance. When examining these relationships separately by gender, a strong association was found for males between active social media use and outspokenness, whereas passive social media use and outspokenness were negatively correlated for females. The results of this study add to previous research in the field of social media and outspokenness and lend new ideas for future research on these topics, such as exploring the gender differences that are associated with these variables. Further research in the area is needed for a more complete understanding of how one’s social media use affects his/her outspokenness and how gender modifies these effects.
ContributorsRubino, Kelli Erika (Co-author) / Rubino, Kelli (Co-author) / Mickelson, Kristin (Thesis director) / Halavais, Alexander (Committee member) / Department of Psychology (Contributor) / School of International Letters and Cultures (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
Social media has become a direct and effective means of transmitting personal opinions into the cyberspace. The use of certain key-words and their connotations in tweets portray a meaning that goes beyond the screen and affects behavior. During terror attacks or worldwide crises, people turn to social media as a means of managing their anxiety, a mechanism of Terror Management Theory (TMT). These opinions have distinct impacts on the emotions that people express both online and offline through both positive and negative sentiments. This paper focuses on using sentiment analysis on twitter hash-tags during five major terrorist attacks that created a significant response on social media, which collectively show the effects that 140-character tweets have on perceptions in social media. The purpose of analyzing the sentiments of tweets after terror attacks allows for the visualization of the effect of key-words and the possibility of manipulation by the use of emotional contagion. Through sentiment analysis, positive, negative and neutral emotions were portrayed in the tweets. The keywords detected also portray characteristics about terror attacks which would allow for future analysis and predictions in regards to propagating a specific emotion on social media during future crisis.
ContributorsHarikumar, Swathikrishna (Author) / Davulcu, Hasan (Thesis director) / Bodford, Jessica (Committee member) / Computer Science and Engineering Program (Contributor) / Department of Information Systems (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12
Description
Proliferation of social media websites and discussion forums in the last decade has resulted in social media mining emerging as an effective mechanism to extract consumer patterns. Most research on social media and pharmacovigilance have concentrated on
Adverse Drug Reaction (ADR) identification. Such methods employ a step of drug search followed by classification of the associated text as consisting an ADR or not. Although this method works efficiently for ADR classifications, if ADR evidence is present in users posts over time, drug mentions fail to capture such ADRs. It also fails to record additional user information which may provide an opportunity to perform an in-depth analysis for lifestyle habits and possible reasons for any medical problems.
Pre-market clinical trials for drugs generally do not include pregnant women, and so their effects on pregnancy outcomes are not discovered early. This thesis presents a thorough, alternative strategy for assessing the safety profiles of drugs during pregnancy by utilizing user timelines from social media. I explore the use of a variety of state-of-the-art social media mining techniques, including rule-based and machine learning techniques, to identify pregnant women, monitor their drug usage patterns, categorize their birth outcomes, and attempt to discover associations between drugs and bad birth outcomes.
The technique used models user timelines as longitudinal patient networks, which provide us with a variety of key information about pregnancy, drug usage, and post-
birth reactions. I evaluate the distinct parts of the pipeline separately, validating the usefulness of each step. The approach to use user timelines in this fashion has produced very encouraging results, and can be employed for a range of other important tasks where users/patients are required to be followed over time to derive population-based measures.
Adverse Drug Reaction (ADR) identification. Such methods employ a step of drug search followed by classification of the associated text as consisting an ADR or not. Although this method works efficiently for ADR classifications, if ADR evidence is present in users posts over time, drug mentions fail to capture such ADRs. It also fails to record additional user information which may provide an opportunity to perform an in-depth analysis for lifestyle habits and possible reasons for any medical problems.
Pre-market clinical trials for drugs generally do not include pregnant women, and so their effects on pregnancy outcomes are not discovered early. This thesis presents a thorough, alternative strategy for assessing the safety profiles of drugs during pregnancy by utilizing user timelines from social media. I explore the use of a variety of state-of-the-art social media mining techniques, including rule-based and machine learning techniques, to identify pregnant women, monitor their drug usage patterns, categorize their birth outcomes, and attempt to discover associations between drugs and bad birth outcomes.
The technique used models user timelines as longitudinal patient networks, which provide us with a variety of key information about pregnancy, drug usage, and post-
birth reactions. I evaluate the distinct parts of the pipeline separately, validating the usefulness of each step. The approach to use user timelines in this fashion has produced very encouraging results, and can be employed for a range of other important tasks where users/patients are required to be followed over time to derive population-based measures.
ContributorsChandrashekar, Pramod Bharadwaj (Author) / Davulcu, Hasan (Thesis advisor) / Gonzalez, Graciela (Thesis advisor) / Hsiao, Sharon (Committee member) / Arizona State University (Publisher)
Created2016
Description
With a prison population that has grown to 1.4 million, an imprisonment rate of 419 per 100,000 U.S. residents, and a recidivism rate of 52.2% for males and 36.4% for females, the United States is facing a crisis. Currently, no sufficient measures have been taken by the United States to reduce recidivism. Attempts have been made, but they ultimately failed. Recently, however, there has been an increase in experimentation with the concept of teaching inmates basic computer skills to reduce recidivism. As labor becomes increasingly digitized, it becomes more difficult for inmates who spent a certain period away from technology to adapt and find employment. At the bare minimum, anybody entering the workforce must know how to use a computer and other technological appliances, even in the lowest-paid positions. By incorporating basic computer skills and coding educational programs within prisons, this issue can be addressed, since inmates would be better equipped to take on a more technologically advanced labor market.<br/>Additionally, thoroughly preparing inmates for employment is a necessity because it has been proven to reduce recidivism. Prisons typically have some work programs; however, these programs are typically outdated and prepare inmates for fields that may represent a difficult employment market moving forward. On the other hand, preparing inmates for tech-related fields of work is proving to be successful in the early stages of experimentation. A reason for this success is the growing demand. According to the U.S. Bureau of Labor Statistics, employment in computer and information technology occupations is projected to grow 11 percent between 2019 and 2029. This is noteworthy considering the national average for growth of all other jobs is only 4 percent. It also warrants the exploration of educating coders because software developers, in particular, have an expected growth rate of 22 percent between 2019 and 2029. <br/>Despite the security risks of giving inmates access to computers, the implementation of basic computer skills and coding in prisons should be explored further. Programs that give inmates access to a computing education already exist. The only issue with these programs is their scarcity. However, this is to no fault of their own, considering the complex nature and costs of running such a program. Accordingly, this leaves the opportunity for public universities to get involved. Public universities serve as perfect hosts because they are fully capable of leveraging the resources already available to them. Arizona State University, in particular, is a more than ideal candidate to spearhead such a program and serve as a model for other public universities to follow. Arizona State University (ASU) is already educating inmates in local Arizona prisons on subjects such as math and English through their PEP (Prison Education Programming) program.<br/>This thesis will focus on Arizona specifically and why this would benefit the state. It will also explain why Arizona State University is the perfect candidate to spearhead this kind of program. Additionally, it will also discuss why recidivism is detrimental and the reasons why formerly incarcerated individuals re-offend. Furthermore, it will also explore the current measures being taken in Arizona and their limitations. Finally, it will provide evidence for why programs like these tend to succeed and serve as a proposal to Arizona State University to create its own program using the provided framework in this thesis.
ContributorsAwawdeh, Bajis Tariq (Author) / Halavais, Alexander (Thesis director) / Funk, Kendall (Committee member) / School of Social and Behavioral Sciences (Contributor, Contributor) / School of Humanities, Arts, and Cultural Studies (Contributor) / Sandra Day O'Connor College of Law (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
Social media has become a primary means of communication and a prominent source of information about day-to-day happenings in the contemporary world. The rise in the popularity of social media platforms in recent decades has empowered people with an unprecedented level of connectivity. Despite the benefits social media offers, it also comes with disadvantages. A significant downside to staying connected via social media is the susceptibility to falsified information or Fake News. Easy accessibility to social media and lack of truth verification tools favored the miscreants on online platforms to spread false propaganda at scale, ensuing chaos. The spread of misinformation on these platforms ultimately leads to mistrust and social unrest. Consequently, there is a need to counter the spread of misinformation which could otherwise have a detrimental impact on society. A notable example of such a case is the 2019 Covid pandemic misinformation spread, where coordinated misinformation campaigns misled the public on vaccination and health safety. The advancements in Natural Language Processing gave rise to sophisticated language generation models that can generate realistic-looking texts. Although the current Fake News generation process is manual, it is just a matter of time before this process gets automated at scale and generates Neural Fake News using language generation models like the Bidirectional Encoder Representations from Transformers (BERT) and the third generation Generative Pre-trained Transformer (GPT-3). Moreover, given that the current state of fact verification is manual, it calls for an urgent need to develop reliable automated detection tools to counter Neural Fake News generated at scale. Existing tools demonstrate state-of-the-art performance in detecting Neural Fake News but exhibit a black box behavior. Incorporating explainability into the Neural Fake News classification task will build trust and acceptance amongst different communities and decision-makers. Therefore, the current study proposes a new set of interpretable discriminatory features. These features capture statistical and stylistic idiosyncrasies, achieving an accuracy of 82% on Neural Fake News classification. Furthermore, this research investigates essential dependency relations contributing to the classification process. Lastly, the study concludes by providing directions for future research in building explainable tools for Neural Fake News detection.
ContributorsKarumuri, Ravi Teja (Author) / Liu, Huan (Thesis advisor) / Corman, Steven (Committee member) / Davulcu, Hasan (Committee member) / Arizona State University (Publisher)
Created2022
Description
Social media has been extensively researched, and its effects on well-being are well established. What is less studied, however, is how social media affects romantic relationships specifically. The few studies that have researched this have found mixed results. Some researchers have found social media to have a positive influence on relationship outcomes, while other have found social media to have a negative influence. In an attempt to reconcile these discrepancies, the current thesis study explored possible mediators between social media use and relationship health outcomes which, to my knowledge, has not been investigated in previous literature. Three moderators were explored: type of social media use (active use versus passive use), relationship-contingent self-esteem, and social comparison orientation. The baseline portion of the study had 547 individuals, recruited from Arizona State University’s SONA system as well as Amazon’s Mechanical Turk, who were in a romantic relationship for at least three months; the follow-up portion of the study had 181 participants. Results suggest that women who passively use social media exhibit a negative association between hours per day of social media use and baseline relationship satisfaction. Men who passively use social media exhibited a negative association between hours per day of social media use and follow-up relationship satisfaction, as well as a negative association with baseline commitment. While relationship-contingent self-esteem did not moderate the association between hours per day of social media use and relationship health, it was positively related to both men and women’s baseline relationship satisfaction and baseline commitment. Social comparison orientation (SCO) produced minimal results; women low on SCO exhibited a negative association between social media use and baseline relationship satisfaction, and higher SCO for men was associated with lower baseline commitment. Finally, exploratory post-hoc mediation models revealed that relationship comparisons mediated the association between hours per day of social media use and baseline relationship, as well as baseline commitment, for both men and women. Previous research supports the findings regarding passive social media use, while the findings regarding relationship-contingent self-esteem and relationship comparisons add new findings to the romantic relationship literature.
ContributorsQuiroz, Selena (Author) / Mickelson, Kristin (Thesis advisor) / Burleson, Mary (Committee member) / Halavais, Alexander (Committee member) / Arizona State University (Publisher)
Created2019