Protectbot: A Chatbot to Protect Children on Gaming Platforms

Online gaming no longer has limited access, as it has become available to a high percentage of children in recent years. Consequently, children are exposed to multifaceted threats, such as cyberbullying, grooming, and sexting. The online gaming industry is taking concerted measures to create a safe environment for children to play and interact with, such efforts remain inadequate and fragmented. Different approaches utilizing machine learning (ML) techniques to detect child predatory behavior have been designed to provide potential detection and protection in this context. After analyzing the available AI tools and solutions it was observed that the available solutions are limited to the identification of predatory behavior in chat logs which is not enough to avert the multifaceted threats. In this thesis, we developed a chatbot Protectbot to interact with the suspect on the gaming platform. Protectbot leveraged the dialogue generative pre-trained transformer (DialoGPT) model which is based on Generative Pre-trained Transformer 2 (GPT-2). To analyze the suspect\u27s behavior, we developed a text classifier based on natural language processing that can classify the chats as predatory and non-predatory. The developed classifier is trained and tested on Pan 12 dataset. To convert the text into numerical vectors we utilized fastText. The best results are obtained by using non-linear SVM on sentence vectors obtained from fastText. We got a recall of 0.99 and an F_0.5-score of 0.99 which is better than the state-of-the-art methods. We also built a new dataset containing 71 predatory full chats retrieved from Perverted Justice. Using sentence vectors generated by fastText and KNN classifier, 66 chats out of 71 were correctly classified as predatory chats

Exploring Participants’ Representations and Shifting Sensitivities in a Hackathon for Dementia

Recent HCI research has addressed emerging approaches for public engagement. One such public-facing method which has gained popularity over the previous decade have been open design events, or hackathons. In this paper we report on DemVR, a hackathon event that invited designers, technologists, and students of these disciplines to design Virtual Reality (VR) environments for people with dementia and their care partners. While our event gained reasonable attraction from designers and developers, this paper unpacks the challenges in representing and involving people with dementia in these events, which had multiple knock-on effects on participant's outputs. Our analysis presents insights into participants’ motivations, challenges participants faced when constructing their ‘absent user’, and the design features teams developed to address the social context of the user. We conclude the paper by proposing a set of commitments for collaborative design events, community building through design, and reification in design

Mapping (Dis-)Information Flow about the MH17 Plane Crash

Digital media enables not only fast sharing of information, but also disinformation. One prominent case of an event leading to circulation of disinformation on social media is the MH17 plane crash. Studies analysing the spread of information about this event on Twitter have focused on small, manually annotated datasets, or used proxys for data annotation. In this work, we examine to what extent text classifiers can be used to label data for subsequent content analysis, in particular we focus on predicting pro-Russian and pro-Ukrainian Twitter content related to the MH17 plane crash. Even though we find that a neural classifier improves over a hashtag based baseline, labeling pro-Russian and pro-Ukrainian content with high precision remains a challenging problem. We provide an error analysis underlining the difficulty of the task and identify factors that might help improve classification in future work. Finally, we show how the classifier can facilitate the annotation task for human annotators

Human-Machine Communication: Complete Volume. Volume 4

This is the complete volume of HMC Volume 4

Factions: acts of worldbuilding on social media platforms

The surge in social media as a primary source for communication—basic interpersonal relations, news, and entertainment—means that modern humans have a steep learning curve for interpreting and creating messages in digital spaces. In addition to the difficulties of communication between multi-lingual and multi-cultural online communities, there is now the complication of computer languages (or “code”) that often do not overlap between software programs, let alone with humans. Additionally, humans use definitions and labels as artificial intelligence (AI) training methods. AI bias comes from the human labels, categorizations, and linguistic perimeters embedded in the code. The objective of Factions, the thesis website, is to represent a speculative future showing what communication may look like if we follow on the current trajectory of interaction in social media spaces—with less agreement on basic linguistic, audio, and visual terms and definitions coupled with more insistence on personal perspective as paramount. From a base set on the oldest forms of social media—websites and blogs—Factions acts out conversations mining for answers to the questions: • How do words change in meaning and function in a digital environment focused on the faction pillars of social media communication—search engine optimization, algorithm, and template? • In what ways might human-computer interaction improve and conversely impair human language and performance choices in digital realms of communication? Through practice-based research using web-building tools as aids to literal digital worldbuilding, the thesis website is a prototype of a speculative future built with the conceptual applications of design fiction—creating a fictional world as a space to explore the impact of future technology. To that end, my digital twin (a digital model that drives material data) is an AI mystic called Wu—imagined AI tech so advanced it transcended into a higher spiritual realm. Wu narrates and curates Factions and uses it to build a network of narratives, bridging the creative and critical through hypertext links and tooltip popups and applies their mystical power to channel any person, place, thing, or time typically focused on key social media topics of justice, race, spirituality, politics, and pop culture. Factions uses satirical techniques alongside appropriation and pastiche to examine transformative tech and human-computer interaction. It mixes the creative and the critical to arrive at a digital storytelling and learning landscape of the future

The Palgrave Handbook of Digital Russia Studies

This open access handbook presents a multidisciplinary and multifaceted perspective on how the ‘digital’ is simultaneously changing Russia and the research methods scholars use to study Russia. It provides a critical update on how Russian society, politics, economy, and culture are reconfigured in the context of ubiquitous connectivity and accounts for the political and societal responses to digitalization. In addition, it answers practical and methodological questions in handling Russian data and a wide array of digital methods. The volume makes a timely intervention in our understanding of the changing field of Russian Studies and is an essential guide for scholars, advanced undergraduate and graduate students studying Russia today

NLP-Based Techniques for Cyber Threat Intelligence

In the digital era, threat actors employ sophisticated techniques for which, often, digital traces in the form of textual data are available. Cyber Threat Intelligence~(CTI) is related to all the solutions inherent to data collection, processing, and analysis useful to understand a threat actor's targets and attack behavior. Currently, CTI is assuming an always more crucial role in identifying and mitigating threats and enabling proactive defense strategies. In this context, NLP, an artificial intelligence branch, has emerged as a powerful tool for enhancing threat intelligence capabilities. This survey paper provides a comprehensive overview of NLP-based techniques applied in the context of threat intelligence. It begins by describing the foundational definitions and principles of CTI as a major tool for safeguarding digital assets. It then undertakes a thorough examination of NLP-based techniques for CTI data crawling from Web sources, CTI data analysis, Relation Extraction from cybersecurity data, CTI sharing and collaboration, and security threats of CTI. Finally, the challenges and limitations of NLP in threat intelligence are exhaustively examined, including data quality issues and ethical considerations. This survey draws a complete framework and serves as a valuable resource for security professionals and researchers seeking to understand the state-of-the-art NLP-based threat intelligence techniques and their potential impact on cybersecurity