Detecting Political Framing Shifts and the Adversarial Phrases within\\ Rival Factions and Ranking Temporal Snapshot Contents in Social Media

abstract: Social Computing is an area of computer science concerned with dynamics of communities and cultures, created through computer-mediated social interaction. Various social media platforms, such as social network services and microblogging, enable users to come together and create social movements expressing their opinions on diverse sets of issues, events, complaints, grievances, and goals. Methods for monitoring and summarizing these types of sociopolitical trends, its leaders and followers, messages, and dynamics are needed. In this dissertation, a framework comprising of community and content-based computational methods is presented to provide insights for multilingual and noisy political social media content. First, a model is developed to predict the emergence of viral hashtag breakouts, using network features. Next, another model is developed to detect and compare individual and organizational accounts, by using a set of domain and language-independent features. The third model exposes contentious issues, driving reactionary dynamics between opposing camps. The fourth model develops community detection and visualization methods to reveal underlying dynamics and key messages that drive dynamics. The final model presents a use case methodology for detecting and monitoring foreign influence, wherein a state actor and news media under its control attempt to shift public opinion by framing information to support multiple adversarial narratives that facilitate their goals. In each case, a discussion of novel aspects and contributions of the models is presented, as well as quantitative and qualitative evaluations. An analysis of multiple conflict situations will be conducted, covering areas in the UK, Bangladesh, Libya and the Ukraine where adversarial framing lead to polarization, declines in social cohesion, social unrest, and even civil wars (e.g., Libya and the Ukraine).Dissertation/ThesisDoctoral Dissertation Computer Science 201

‘I’m Not a Virus’: Asian Hate in Donald Trump’s Rhetoric

Since the start of Covid-19, anti-Asian sentiment spiked. From March 2020 to June 2021, there were a total of 9,081 self-reported incidents of hate across the United States (Stop AAPI Hate. (2021). As Covid-19 spread into the U.S., President Trump immediately blamed China by referring to the virus as the ‘Chinese Virus’ and used the hashtag #ChineseVirus on Twitter (Weise, E. 2021). Anti-Asian hashtags soared after Donald Trump first tied COVID-19 to China on Twitter. (USA Today. https://www. usatoday.com). Anti-Asian rhetoric expressed on Twitter grew after Trump’s tweet about the ‘Chinese virus,’ and the number of Chinese and other Asian hate crimes grew exponentially. This study explores the rhetorical strategies that Trump utilized to create a sense of fear against the dangerous ‘Other.’ We use a rhetorical thematic analysis to analyze Trump’s tweets that contain language such as ‘Chinese virus’ or ‘Kung Flu.’ Themes such as scapegoating, fear of the other, China bashing, and populist appeals were prevalent. Describing Chinese and other Asian bodies as ‘spreaders’ of diseases, reinforces the Yellow Peril and perpetual foreigner stereotypes. The study shows the importance of presidential rhetoric in influencing public opinion in the context of COVID-19 and Asian hate

Visual Analytics for Social Business Intelligence - Tool Prototype Demo

Today, social media is widely adopted across personal and professional spheres. Increasingly businesses are utilizing social media as part of their strategy for communicating with and understanding the behaviors of their clients. The widespread public use of social media is a relatively new phenomenon that presents an ongoing, ever-changing challenge to companies and creates a unique set of risks as well as advantages to decision-makers. At the same time expansion into the online social space offers tremendous potential strategic advantages including demographic targeting from a new, pervasive reflection of consumers and brand advocates. Social media thus takes on a new relevance in forging relationships of brand co-creation. This research project, in its entirety, seeks to derive business value from social data by designing and developing a series of dashboards for those who struggle to interpret and keep up with the social data created around a brand and marketing campaign. This tool prototype demo first outlines the foundation of the tool development with focus on the main perspectives guiding the research. A presentation of the actual tool development is subsequently put forward highlighting the main components of the tool. Challenges are discussed therein before a brief conclusion of the prototype development thus far

IDRC global symposium on AI & inclusion outputs

Artificial intelligence (AI) and related technologies have begun to shape important parts of the digital economy and already affect core areas of increasingly networked societies. Uneven access to and impacts of AI-based technologies on marginalized populations could amplify global digital inequalities. This complete set of materials serves as input for the IDRC research agenda “Artificial Intelligence and Human Development." The cover memo is followed by an analysis of research questions at the intersection of AI, definition of terms such as “inclusion” (where inclusion is re-framed to embrace self-determination), as well as appendices of additional outputs from various symposia

MANAGING INFORMATION DIFFUSION IN ONLINE SOCIAL NETWORKS VIA STRUCTURAL ANALYSIS

Ph.DDOCTOR OF PHILOSOPH

Using Twitter data to provide qualitative insights into pandemics and epidemics

Background: One area of public health research specialises in examining public views and opinions surrounding infectious disease outbreaks. Although interviews and surveys are valid sources of this information, views and opinions are necessarily generated by the context, rather than spontaneous. As such, social media has increasingly been viewed as legitimate source of pragmatic, unfiltered public opinion. Objectives: This research attempts to better understand how users converse about infectious disease outbreaks on the social media platform Twitter. The study was undertaken in order to address a gap in knowledge because previous empirical studies that have analysed infectious disease outbreaks on Twitter have focused on employing quantitative methods as the primary form of data analysis. After analysing individual cases on Ebola, Zika, and swine flu, the study performs an important comparison in the types of discussions taking place on Twitter and is the first empirical study to do so. Methods: A number of pilot studies were initially designed and conducted in order to help inform the main study. The study then manually labels tweets on infectious disease outbreaks assisted by the qualitative analysis programme NVivo, and performs an analysis using the Health Belief Model, concepts around information theory, and a number of sociological principles. The data were purposively sampled according to when Google Trends Data showed a heightened interest in the respective outbreaks, and a case study approach was utilised. Results: A substantial number of themes were uncovered which were not reported in previous literature, demonstrating the potential of qualitative methodologies for extracting greater insight into public health opinions from Twitter data. The study noted several limitations of Twitter data for use in qualitative research. However, results demonstrated the potential of Twitter to identify discussions around infectious diseases that might not emerge in an interview and/or which might not be included in a survey

Is Every Tweet Created Equal? A Framework to Identify Relevant Tweets for Business Research

It is a life or death matter for a firm to observe its environment and identify new threats or opportunities quickly. Information technology has increased firm’s speed and agility in responding to environmental changes. Social media offers a vast and timely source of environmental information that firms can readily use gauge public sentiment. Twitter is a high-speed service that allows anyone to “tweet” a message to any interested parties. Firms can access near instantaneous changes in the public mood about any topic by using Sentiment Analysis. These topics range from predicting equities prices to predicting election outcomes. A gap exists in the literature because researchers discard tweets without any theoretically sound reason for doing so. We propose a framework that provides a theory-based justification for discarding data. We then explore the framework results using high frequency equity market prices. By examining the results of three case studies encompassing 57,600 OLS regressions and 1,887,408 tweets, our results indicate the framework yields higher quality results as measured by better R2 fits

Obstacles to wearable computing

In the year 2021, wearable technology could look beautiful and feel magical, but instead is exemplified by a plain wristband that looks suspiciously like a prison monitor. How can we make wearable technology that respects our privacy, enhances our daily lives, integrates with our other connected devices without leashing us to a smartphone, and visually expresses who we are? This study uses a novel method of participatory design fiction (PDFi) to understand potential users of everyday wearable technology through storytelling. I recruited participants from the general public and gave them a five-point prompt to create a design fiction (DF), which inspired the user-centred design of an everyday connected wearable device. The participants each received a technology probe to wear in the wild for a year. They then updated their DFs as a way to reflect on the implications of the technology. For the purposes of privacy, augmenting device functionality through interoperability, and integration into an Internet of Things (IoT) ecosystem, I used the Hub-of-All-Things personal data store to provide the software infrastructure. By listening to their stories, we can elicit design concepts directly from the users, to help us create wearable IoT devices that put the wearer at the centre of the design process, and are satisfying both functionally and emotionally.The Alan Turing Institute Doctoral Scheme, University of Cambridge Department of Computer Science and Technology, The Kenneth Hayter Memorial Fun