DYNAMICS OF IDENTITY THREATS IN ONLINE SOCIAL NETWORKS: MODELLING INDIVIDUAL AND ORGANIZATIONAL PERSPECTIVES

This dissertation examines the identity threats perceived by individuals and organizations in Online Social Networks (OSNs). The research constitutes two major studies. Using the concepts of Value Focused Thinking and the related methodology of Multiple Objectives Decision Analysis, the first research study develops the qualitative and quantitative value models to explain the social identity threats perceived by individuals in Online Social Networks. The qualitative value model defines value hierarchy i.e. the fundamental objectives to prevent social identity threats and taxonomy of user responses, referred to as Social Identity Protection Responses (SIPR), to avert the social identity threats. The quantitative value model describes the utility of the current social networking sites and SIPR to achieve the fundamental objectives for averting social identity threats in OSNs. The second research study examines the threats to the external identity of organizations i.e. Information Security Reputation (ISR) in the aftermath of a data breach. The threat analysis is undertaken by examining the discourses related to the data breach at Home Depot and JPMorgan Chase in the popular microblogging website, Twitter, to identify: 1) the dimensions of information security discussed in the Twitter postings; 2) the attribution of data breach responsibility and the related sentiments expressed in the Twitter postings; and 3) the subsequent diffusion of the tweets that threaten organizational reputation

Examining Canada’s Scientific Literacy Through COVID-19 Tweets

Scientific misinformation spread on social media is a concern for science communicators, health communicators, and science educators alike. During the COVID-19 pandemic, the World Health Organization (WHO) released a statement that modern technology has created an infodemic, undermining the COVID-19 response effort. Misinformation spread online threatens public health and can endanger lives. So how do we combat it? The leading solution is education, in particular, equipping individuals with scientific literacy. Scientific literacy, or the ability to critically evaluate, understand, and make decisions regarding scientific information, is the goal of science curriculums globally. There has been much research over the past couple of decades regarding the usage of scientific literacy in formal learning environments. In contrast, the relationship between scientific literacy and online informal learning environments such as social media is not well understood. Our case study sought to help fill this gap in the research by exploring how Canadians employ scientific literacy on Twitter—a popular social media site—when discussing the COVID-19 pandemic. We conducted an exploratory qualitative case study exploring 2 600 tweets originating from accounts with user locations in Canada and shared on Twitter during the first ten months of the pandemic (March 2020 to December 2020) to see whether and how they displayed scientific literacy. In addition,­­ we examined the trends and factors that affect the usage of scientific literacy online. Using qualitative content analysis techniques and supplemental statistical analysis, we found that 10% of tweets sampled displayed scientific literacy, while 2% did not exhibit scientific literacy. There were no interprovincial differences in how Canadians displayed scientific literacy, with all provinces sampled exhibiting scientific literacy in approximately 10% of tweets. Furthermore, scientific literacy was not affected by how often the user tweeted, how many followers they had, or the month the tweet was shared. We discovered a strong relationship between the tweet\u27s topic and if it displayed scientific literacy or a lack of scientific literacy. Our study provides more insight into how scientific literacy is displayed online. Future researchers can use this as a starting point to conduct studies exploring how scientific literacy is employed in online spaces in different locations and contexts globally

Social Media and Information Polarization: Amplifying Echoes or Extremes?

Social media sites are widely believed to solidify our echo chambers by sorting the political information liberal and conservative Americans are exposed to, leaving partisans and ideologues in information bubbles where their beliefs are confirmed and their views go uncontested. This dissertation challenges that common understanding of information polarization on social media and instead proposes that social media polarizes not by exposing users to congruent information but rather to extreme information. In examining the potential mechanisms of information sorting on social media – the flow of information in homophilous networks and users’ choices in who they connect to and what to share – I find that social media sites are poorly equipped to filter out challenging information. Indeed, they may be better designed to expose users to more ideologically diverse information than those users encounter on other media on and offline. Rather than solidify our echo chambers, I propose that social media sites polarize our information environments by amplifying the prevalence of what I term “extreme” news - news that is ideologically dogmatic, emotionally-evocative and tribal in nature. Like “information sorting” and the creation of echo chambers, “information extreming” can have damaging consequences for civil society and democracy; the exposure to extreme information from both sides of the political spectrum leaves partisans and ideologues more certain in their correctness and in the illegitimacy of their political opponents. In the second part of the dissertation I examine one of the levers of information polarization – social media users’ curation biases – and present a theoretical framework to explain users’ motivations for sharing confirming and extreme information. That framework proposes that users are motivated to both signal solidarity with their political groups and, in times of threat, to rally against outgroups. Online experimental tests provide confirmation for elements of the framework, but much work is left to be done to understand what drives users to share congruent and extreme information.PHDPolitical ScienceUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/153422/1/julkamin_1.pd

Factors that motivate South African students to share fake news on social media platforms

Dissertation (MIT (Information Systems) )--University of Pretoria, 2021.The increased adoption of social media and the continued spread of fake news has resulted in unique problems for society to overcome in the modern era. This study aims to determine what factors influence South African students to share fake news on social media platforms. The theory that was used to create the research model and questionnaire was the Users and Gratification (U&G) framework. A mixed-method approach was followed in conducting the study, utilising both quantitative and qualitative strategies. Data was gathered through collecting responses using a questionnaire distributed to students of the EBIT faculty at the University of Pretoria. 190 usable responses were gathered. The questionnaire was created using Google forms and the questionnaire link was shared to students through clickUP and various student groups on Facebook. The factors that were investigated were platform, emotional drivers, social responsibility, conformity, biases, trust, third-person perspective (TPP) and personality and how they influence intention to share fake news among students. The findings from the empirical study of 190 students found that the hypothesis that there is a positive association between bias and trust was partially supported. There was also found to be a negative correlation between third-person perspective, emotional drivers, and the conscientiousness trait of the big-five personality model. This confirms that people’s emotional drive, bias, TPP, trust, and conscientiousness have a moderate effect on their intention to share. Additionally, from the qualitative findings, the factors of previous experience and knowledge were also found to influence intention to share. Through partial least squares regression analysis, we found that the factors that contributed the most to intention to share are emotional influences and the conscientiousness trait of personality that both had a negative association. TPP has small correlations to intention to share. Trust and bias were removed from the quantitative model due to bad fit, however, from the qualitative findings it was determined that trust and bias impacted students’ identification of fake news articles. By understanding the relationship between TPP, conscientiousness, trust, bias, emotional drivers, previous experience, previous knowledge and intention to share fake news may help further the understanding of why fake news is spread, the motivation for students to share fake news and curb the spread with changing technological environments. These findings can also promote action to implement programs and regulations to protect users who are vulnerable and more exposed to fake news on social media platforms.InformaticsMIT (Information Systems)Unrestricte

A multi-disciplinary co-design approach to social media sensemaking with text mining

This thesis presents the development of a bespoke social media analytics platform called Sentinel using an event driven co-design approach. The performance and outputs of this system, along with its integration into the routine research methodology of its users, were used to evaluate how the application of an event driven co-design approach to system design improves the degree to which Social Web data can be converted into actionable intelligence, with respect to robustness, agility, and usability. The thesis includes a systematic review into the state-of-the-art technology that can support real-time text analysis of social media data, used to position the text analysis elements of the Sentinel Pipeline. This is followed by research chapters that focus on combinations of robustness, agility, and usability as themes, covering the iterative developments of the system through the event driven co-design lifecycle. Robustness and agility are covered during initial infrastructure design and early prototyping of bottom-up and top-down semantic enrichment. Robustness and usability are then considered during the development of the Semantic Search component of the Sentinel Platform, which exploits the semantic enrichment developed in the prototype, alpha, and beta systems. Finally, agility and usability are used whilst building upon the Semantic Search functionality to produce a data download functionality for rapidly collecting corpora for further qualitative research. These iterations are evaluated using a number of case studies that were undertaken in conjunction with a wider research programme, within the field of crime and security, that the Sentinel platform was designed to support. The findings from these case studies are used in the co-design process to inform how developments should evolve. As part of this research programme the Sentinel platform has supported the production of a number of research papers authored by stakeholders, highlighting the impact the system has had in the field of crime and security researc

Building a Call to Action: Social Action in Networks of Practice

The three research papers completed as part of this dissertation explore how people contributing to #BlackLivesMatter build knowledge, using social construction of knowledge (SCK), and what they are building knowledge about, using critical consciousness, because understanding how these processes play out on Twitter provides a way for others to understand this social movement. Paper 1 describes a new methodological approach to combining social network analysis (SNA) and social learning analytics to assess SCK. The sequential mixed method design begins by conducting a content analysis according to the Interaction Analysis Model (IAM). The results of the content analysis yield descriptive data that can be used to conduct SNA and social learning analytics. The purpose of Paper 2 was to use the typology of digital activism actions identified by Penney and Dadas (2014) from interviews with digital activists to validate them in a quantitative study. Paper 2 found that the actions taken by people who are helping to facilitate face-to-face action (p \u3c .0000001 , r = -0.076) or provide face-to-face updates (p \u3c .0000001 , r = -0.060) were negatively correlated with the actions of people who were facilitating online actions suggesting that digital activists should be treated as a unique population of activists. Paper 3 used the outcomes of a content analysis and lexicon analysis performed on #BlackLivesMatter data to determine 1) the levels of SCK and critical consciousness present in online data and 2) social learning analytics to ascertain the extent that SCK and critical consciousness can predict social action. Results of the content analysis and lexicon analysis found all levels of SCK and critical consciousness in the data. Results of social learning analytics conducted using Naïve Bayes classification indicate that SCK and critical consciousness can only predict information sharing behaviors of online social action like personal opinions, forwarding information, and engaging in discussion. Evidence of information sharing behaviors on Twitter provides a high degree of confidence that further research including replies and other interactions between users will reveal robust SCK

Assessing the social impacts of extreme weather events using social media

The frequency and severity of extreme weather events such as flooding, hurricanes/storms and heatwaves are increasing as a result of climate change. There is a need for information to better understand when, where and how these events are impacting people. However, there are currently limited sources of impact information beyond traditional meteorological observations. Social sensing, which is the use of unsolicited social media data to better understand real world events, is one method that may provide such information. Social sensing has successfully been used to detect earthquakes, floods, hurricanes, wildfires, heatwaves and other weather hazards. Here social sensing methods are adapted to explore potential for collecting impact information for meteorologists and decision makers concerned with extreme weather events. After a review of the literature, three experimental studies are presented. Social sensing is shown to be effective for detection of impacts of named storms in the UK and Ireland. Topics of discussion and sentiment are explored in the period before, during and after a storm event. Social sensing is also shown able to detect high-impact rainfall events worldwide, validating results against a manually curated database. Additional events which were not known to this database were found by social sensing. Finally, social sensing was applied to heatwaves in three European cities. Building on previous work on heatwaves in the UK, USA and Australia, the methods were extended to include impact phrases alongside hazard-related phrases, in three different languages (English, Dutch and Greek). Overall, social sensing is found to be a good source of impact information for organisations that need to better understand the impacts of extreme weather. The research described in this project has been commercialised for operational use by meteorological agencies in the UK, including the Met Office, Environment Agency and Natural Resources Wales.Engineering and Physical Sciences Research Council (EPSRC