Computer Vision to Analyze Protests in Social Media

Images are central to understanding protests and mass activism today for its impact in shaping public opinion. Previously, analyzing protest images required human annotation, which is laborious and expensive. In the modern era of social media, an automated and systematic method is required to analyze the vast amounts of social media images. In this thesis, I introduce a deep-learning computational framework to analyze protest images. This system comprises of (1) processing and parsing social media images from Twitter, (2) a model to identify common protest image characteristics, such as violence, fire, and police, models to (3) detect and (4) classify faces of protesters to understand demographics, and (5) an deduplication algorithm to identify the most shared images

Violence Detection in Social Media-Review

Social media has become a vital part of humans’ day to day life. Different users engage with social media differently. With the increased usage of social media, many researchers have investigated different aspects of social media. Many examples in the recent past show, content in the social media can generate violence in the user community. Violence in social media can be categorised into aggregation in comments, cyber-bullying and incidents like protests, murders. Identifying violent content in social media is a challenging task: social media posts contain both the visual and text as well as these posts may contain hidden meaning according to the users’ context and other background information. This paper summarizes the different social media violent categories and existing methods to detect the violent content.Keywords: Machine learning, natural language processing, violence, social media, convolution neural networ

A Quantitative Approach to Understanding Online Antisemitism

A new wave of growing antisemitism, driven by fringe Web communities, is an increasingly worrying presence in the socio-political realm. The ubiquitous and global nature of the Web has provided tools used by these groups to spread their ideology to the rest of the Internet. Although the study of antisemitism and hate is not new, the scale and rate of change of online data has impacted the efficacy of traditional approaches to measure and understand these troubling trends. In this paper, we present a large-scale, quantitative study of online antisemitism. We collect hundreds of million posts and images from alt-right Web communities like 4chan's Politically Incorrect board (/pol/) and Gab. Using scientifically grounded methods, we quantify the escalation and spread of antisemitic memes and rhetoric across the Web. We find the frequency of antisemitic content greatly increases (in some cases more than doubling) after major political events such as the 2016 US Presidential Election and the "Unite the Right" rally in Charlottesville. We extract semantic embeddings from our corpus of posts and demonstrate how automated techniques can discover and categorize the use of antisemitic terminology. We additionally examine the prevalence and spread of the antisemitic "Happy Merchant" meme, and in particular how these fringe communities influence its propagation to more mainstream communities like Twitter and Reddit. Taken together, our results provide a data-driven, quantitative framework for understanding online antisemitism. Our methods serve as a framework to augment current qualitative efforts by anti-hate groups, providing new insights into the growth and spread of hate online.Comment: To appear at the 14th International AAAI Conference on Web and Social Media (ICWSM 2020). Please cite accordingl

Understanding peace through the world news

Peace is a principal dimension of well-being and is the way out of inequity and violence. Thus, its measurement has drawn the attention of researchers, policymakers, and peacekeepers. During the last years, novel digital data streams have drastically changed the research in this field. The current study exploits information extracted from a new digital database called Global Data on Events, Location, and Tone (GDELT) to capture peace through the Global Peace Index (GPI). Applying predictive machine learning models, we demonstrate that news media attention from GDELT can be used as a proxy for measuring GPI at a monthly level. Additionally, we use explainable AI techniques to obtain the most important variables that drive the predictions. This analysis highlights each country’s profile and provides explanations for the predictions, and particularly for the errors and the events that drive these errors. We believe that digital data exploited by researchers, policymakers, and peacekeepers, with data science tools as powerful as machine learning, could contribute to maximizing the societal benefits and minimizing the risks to peace.Peace is a principal dimension of well-being and is the way out of inequity and violence. Thus, its measurement has drawn the attention of researchers, policymakers, and peacekeepers. During the last years, novel digital data streams have drastically changed the research in this field. The current study exploits information extracted from a new digital database called Global Data on Events, Location, and Tone (GDELT) to capture peace through the Global Peace Index (GPI). Applying predictive machine learning models, we demonstrate that news media attention from GDELT can be used as a proxy for measuring GPI at a monthly level. Additionally, we use explainable AI techniques to obtain the most important variables that drive the predictions. This analysis highlights each country’s profile and provides explanations for the predictions, and particularly for the errors and the events that drive these errors. We believe that digital data exploited by researchers, policymakers, and peacekeepers, with data science tools as powerful as machine learning, could contribute to maximizing the societal benefits and minimizing the risks to peace

Approaching Protest with Affect: An Analysis of the Images Spread by News Media During the George Floyd Protests

The purpose of this thesis is to examine the characteristics of images that are most prevalent in news media coverage of the George Floyd Protests during 2020. To do so, I have examined gallery images from nine different news source which cover the gamut of the entire political spectrum. Through my research, it was determined that the characteristics found in the images correlated greatly with the political leanings of the publication, with right-wing publications far more likely to depict scenes of meaningless violence, and left-wing publications far more likely to show linguistic messaging and images of group solidarity. In conclusion, my research shows that affective responses to visual stimuli greatly determines whether an image circulates on a particular news media publication, and suggests way social justice activists can use affect and visual rhetorics to spread their message

Peering through the Kaleidoscope: Variation and Validity in Data Collection on Terrorist Attacks

The nature of underreporting terrorism in developing countries is often acknowledged but poorly understood. Focusing on India, we triangulate terrorist attacks captured across three media-based datasets (GTD, SATP, WITS) against official police records from Andhra Pradesh. Results suggest that media-based datasets capture the geographic prevalence of terrorism yet severely underestimate the frequency of violence, biasing towards lethal bombings. Considerable variation is present for attacks targeting specific classes or types of actors. Similar to other crimes, the results suggest that existing terrorism databases represent a select version of violence in these countries, discounting the prevalence and regularity of non-lethal violent activity