$1.00 per RT #BostonMarathon #PrayForBoston: analyzing fake content on Twitter

This study found that 29% of the most viral content on Twitter during the Boston bombing crisis were rumors and fake content.AbstractOnline social media has emerged as one of the prominent channels for dissemination of information during real world events. Malicious content is posted online during events, which can result in damage, chaos and monetary losses in the real world. We analyzed one such media i.e. Twitter, for content generated during the event of Boston Marathon Blasts, that occurred on April, 15th, 2013. A lot of fake content and malicious profiles originated on Twitter network during this event. The aim of this work is to perform in-depth characterization of what factors influenced in malicious content and profiles becoming viral. Our results showed that 29% of the most viral content on Twitter, during the Boston crisis were rumors and fake content; while 51% was generic opinions and comments; and rest was true information. We found that large number of users with high social reputation and verified accounts were responsible for spreading the fake content. Next, we used regression prediction model, to verify that, overall impact of all users who propagate the fake content at a given time, can be used to estimate the growth of that content in future. Many malicious accounts were created on Twitter during the Boston event, that were later suspended by Twitter. We identified over six thousand such user profiles, we observed that the creation of such profiles surged considerably right after the blasts occurred. We identified closed community structure and star formation in the interaction network of these suspended profiles amongst themselves

Online Textual Hate Content Recognition using Fine-tuned Transformer Models

The popularity, anonymity, and easy accessibility of social media have enabled it as a convenient platform to outspread hate speech. Hate speech can take many forms, viz., racial, political, LGBTQ+, religious, gender-based, nationality-based, etc., overlapping and intersecting with numerous forms of persecution and discrimination, leading to severe harmful impacts on society. It has become crucial to address the problem of online hate speech and create an inclusive and safe online environment. Several techniques have already been investigated to address the issue of online hate speech and have obtained reasonable results. But, their contextual understanding needs to be stronger, and it is quite a complex task as they need larger datasets to take complete advantage of the model’s architecture. In this work, we explored the usage of transformer-based pre-trained models, particularly Bidirectional Encoder Representations from Transformers (BERT) and Robustly Optimized BERT (RoBERTa), to fine-tune them further to detect online hate speech efficiently. Our approach performed well and improved Accuracy and F1-score metrics results by 9.65 percent, Precision and Recall by 10.28 and 8.96 percent, respectively, compared to state-of-art methods with a subsampled dataset, limited resources and time

When Infodemic Meets Epidemic: a Systematic Literature Review

Epidemics and outbreaks present arduous challenges requiring both individual and communal efforts. Social media offer significant amounts of data that can be leveraged for bio-surveillance. They also provide a platform to quickly and efficiently reach a sizeable percentage of the population, hence their potential impact on various aspects of epidemic mitigation. The general objective of this systematic literature review is to provide a methodical overview of the integration of social media in different epidemic-related contexts. Three research questions were conceptualized for this review, resulting in over 10000 publications collected in the first PRISMA stage, 129 of which were selected for inclusion. A thematic method-oriented synthesis was undertaken and identified 5 main themes related to social media enabled epidemic surveillance, misinformation management, and mental health. Findings uncover a need for more robust applications of the lessons learned from epidemic post-mortem documentation. A vast gap exists between retrospective analysis of epidemic management and result integration in prospective studies. Harnessing the full potential of social media in epidemic related tasks requires streamlining the results of epidemic forecasting, public opinion understanding and misinformation propagation, all while keeping abreast of potential mental health implications. Pro-active prevention has thus become vital for epidemic curtailment and containment

Towards a National Security Analysis Approach via Machine Learning and Social Media Analytics

Various severe threats at national and international level, such as health crises, radicalisation, or organised crime, have the potential of unbalancing a nation's stability. Such threats impact directly on elements linked to people's security, known in the literature as human security components. Protecting the citizens from such risks is the primary objective of the various organisations that have as their main objective the protection of the legitimacy, stability and security of the state. Given the importance of maintaining security and stability, governments across the globe have been developing a variety of strategies to diminish or negate the devastating effects of the aforementioned threats. Technological progress plays a pivotal role in the evolution of these strategies. Most recently, artificial intelligence has enabled the examination of large volumes of data and the creation of bespoke analytical tools that are able to perform complex tasks towards the analysis of multiple scenarios, tasks that would usually require significant amounts of human resources. Several research projects have already proposed and studied the use of artificial intelligence to analyse crucial problems that impact national security components, such as violence or ideology. However, the focus of all this prior research was examining isolated components. However, understanding national security issues requires studying and analysing a multitude of closely interrelated elements and constructing a holistic view of the problem. The work documented in this thesis aims at filling this gap. Its main contribution is the creation of a complete pipeline for constructing a big picture that helps understand national security problems. The proposed pipeline covers different stages and begins with the analysis of the unfolding event, which produces timely detection points that indicate that society might head toward a disruptive situation. Then, a further examination based on machine learning techniques enables the interpretation of an already confirmed crisis in terms of high-level national security concepts. Apart from using widely accepted national security theoretical constructions developed over years of social and political research, the second pillar of the approach is the modern computational paradigms, especially machine learning and its applications in natural language processing

Hashtags for Gatekeeping of Information on Social Media

Since the inception of gatekeeping research in the 1940s, most studies on gatekeeping have been human‐centric, treating and studying individuals as gatekeepers, who perform their gatekeeping role using a combination of the following mechanisms: forming communities, and/or broadcasting, discovering‐searching, collecting, organizing, or protecting information. However, the nature of communication channels and how information is produced by and shared with users has fundamentally changed in the last 80 years. One significant change is the growing use of technology‐enabled metadata like hashtags when sharing information on social media. Rarely any study investigates whether hashtags can perform gatekeeping of information and what it means for information gatekeeping. This paper fills in the gap by conducting a content analysis of 77 interdisciplinary studies on hashtags and gatekeeping to confirm how they can implement six gatekeeping mechanisms. This study shows that hashtags expand our understanding of the role of technology solutions in gatekeeping and advance research on hierarchical gatekeeping. The benefits of hashtags for gatekeeping suggest that they act as “information anchors” for online communities, thereby highlighting the utility of information gatekeepers for society

Система обліку надзвичайних подій соціально-політичного характеру

Метою роботи є створення веб-системи обліку надзвичайних ситуацій соціально-політичного характеру з інтерактивною картою України. Система надає звичайним користувачам можливість дослідити дані виниклих надзвичайних ситуацій по різних регіонах України у різні роки. Адміністратор має доступ до створення нових маркерів на карті з внесенням інформації про нові надзвичайні ситуації, а також редагування значення існуючих маркерів.The purpose of this work is to create a web-based system for managing socio-political emergencies data with an interactive map of Ukraine. The system provides regular users with the opportunity to study the data of emergency situations that occurred in different regions of Ukraine in different years. Administrator has access to creating new markers on the map with information about new emergencies, as well as changing the value of existing markers.Целью работы является создание веб-системы учета чрезвычайных ситуаций социально-политического характера с интерактивной картой Украины. Система предоставляет обычным пользователям возможность исследовать данные возникших чрезвычайных ситуаций по разным регионам Украины в разные годы. Администратор имеет доступ к созданию новых маркеров на карте с внесением информации о новые чрезвычайные ситуации, а также редактирования существующих маркеров

Social media as intelligence in disaster response: eyewitness classification using community detection

Disasters cause widespread devastation to both physical infrastructure and the lives of individuals residing in large geographic areas. The disruption caused by disaster events is further compounded by high levels of uncertainty and information scarcity, presenting significant challenges to disaster response organisations and impeding the effectiveness of coordinated response efforts. The increasing use of digital technologies, such as social media, presents valuable sources of information that are available in real-time from geographically-distributed networks of ‘humans as sensors’. The data generated by these technologies can supplement traditional sources of intelligence to build models of situational awareness and inform decision-making, resulting in more effective disaster response operations. This thesis proposes a method of curating social media data to enhance its usefulness as a source of intelligence for disaster response organisations during crisis events. The research was conducted in four phases: (i) An ethnographic study developed a conceptual framework of the values and challenges of social media intelligence as perceived by disaster response practitioners. High data volume and low rates of relevance were established as key factors impeding integration with existing intelligence sources. (ii) Empirical studies of Twitter discourse were conducted during eight disaster events to identify patterns of online behaviour and establish the informative potential of social media data as a rich source of eyewitness reports. (iii) Geoproximate preferential attachment (homophily) was identified in the structure of Twitter relationship networks. An eyewitness classification model integrated relationship features for data curation. The model was evaluated on temporally-partitioned subgraphs and shown to be effective in real-time environments. (iv) The classification model was validated in simulated disaster response scenarios conducted with emergency service practitioners. Feedback from participants confirmed the effectiveness of the approach to improving the practical value of social media data as a source of intelligence during disaster response operations