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1. TUBERAIDER: Attributing Coordinated Hate Attacks on YouTube Videos to Their Source Communities

   https://doi.org/10.1609/icwsm.v18i1.31394  

   Saeed, Mohammad Hammas ; Papadamou, Kostantinos ; Blackburn, Jeremy ; De_Cristofaro, Emiliano ; Stringhini, Gianluca ( May 2024 , Proceedings of the International AAAI Conference on Web and Social Media)

   Alas, coordinated hate attacks, or raids, are becoming increasingly common online. In a nutshell, these are perpetrated by a group of aggressors who organize and coordinate operations on a platform (e.g., 4chan) to target victims on another community (e.g., YouTube). In this paper, we focus on attributing raids to their source community, paving the way for moderation approaches that take the context (and potentially the motivation) of an attack into consideration.We present TUBERAIDER, an attribution system achieving over 75% accuracy in detecting and attributing coordinated hate attacks on YouTube videos. We instantiate it using links to YouTube videos shared on 4chan's /pol/ board, r/The_Donald, and 16 Incels-related subreddits. We use a peak detector to identify a rise in the comment activity of a YouTube video, which signals that an attack may be occurring. We then train a machine learning classifier based on the community language (i.e., TF-IDF scores of relevant keywords) to perform the attribution. We test TUBERAIDER in the wild and present a few case studies of actual aggression attacks identified by it to showcase its effectiveness.

    

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2. Analyzing and Characterizing User Intent in Information-seeking Conversations

   https://doi.org/10.1145/3209978.3210124  

   Qu, Chen ; Yang, Liu ; Croft, W. Bruce ; Trippas, Johanne R. ; Zhang, Yongfeng ; Qiu, Minghui ( July 2018 , Proceedings of the 41st International ACM SIGIR Conference onResearch and Development in Information Retrieval)

   Understanding and characterizing how people interact in information-seeking conversations will be a crucial component in developing effective conversational search systems. In this paper, we introduce a new dataset designed for this purpose and use it to analyze information-seeking conversations by user intent distribution, co-occurrence, and flow patterns. The MSDialog dataset is a labeled conversation dataset of question answering (QA) interactions between information seekers and providers from an online forum on Microsoft products. The dataset contains more than 2,000 multi-turn QA dialogs with 10,000 utterances that are annotated with user intents on the utterance level. Annotations were done using crowdsourcing. With MSDialog, we find some highly recurring patterns in user intent during an information-seeking process. They could be useful for designing conversational search systems. We will make our dataset freely available to encourage exploration of information-seeking conversation models. 

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3. Failure Explanation in Privacy-Sensitive Contexts: An Integrated Systems Approach

   Li, Sihui ; Siva, Sriram ; Mott, Terran ; Williams, Tom ; Zhang, Hao ; Dantam, Neil ( January 2023 , IEEE International Conference on Robot and Human Interactive Communication (RO-MAN))

   In this paper, we explore how robots can properly explain failures during navigation tasks with privacy concerns. We present an integrated robotics approach to generate visual failure explanations, by combining a language-capable cognitive architecture (for recognizing intent behind commands), an object- and location-based context recognition system (for identifying the locations of people and classifying the context in which those people are situated) and an infeasibility proof-based motion planner (for explaining planning failures on the basis of contextually mediated privacy concerns). The behavior of this integrated system is validated using a series of experiments in a simulated medical environment. 

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4. Edited Media Understanding: Reasoning About Implications of Manipulated Images

   Da, Jeff ; Forbes, Maxwell ; Zellers, Rowan ; Zheng, Anthony ; Hwang, Jena D. ; Bosselut, Antoine ; Choi, Yejin ( December 2020 , Association for Computational Linguistics)

   Multimodal disinformation, from `deepfakes' to simple edits that deceive, is an important societal problem. Yet at the same time, the vast majority of media edits are harmless -- such as a filtered vacation photo. The difference between this example, and harmful edits that spread disinformation, is one of intent. Recognizing and describing this intent is a major challenge for today's AI systems. We present the task of Edited Media Understanding, requiring models to answer open-ended questions that capture the intent and implications of an image edit. We introduce a dataset for our task, EMU, with 48k question-answer pairs written in rich natural language. We evaluate a wide variety of vision-and-language models for our task, and introduce a new model PELICAN, which builds upon recent progress in pretrained multimodal representations. Our model obtains promising results on our dataset, with humans rating its answers as accurate 40.35% of the time. At the same time, there is still much work to be done -- humans prefer human-annotated captions 93.56% of the time -- and we provide analysis that highlights areas for further progress. 

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5. Quantifying Intimacy in Language

   https://doi.org/10.18653/v1/2020.emnlp-main.428  

   Pei, Jiaxin ; Jurgens, David ( January 2020 , Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP))

   null (Ed.)

   Intimacy is a fundamental aspect of how we relate to others in social settings. Language encodes the social information of intimacy through both topics and other more subtle cues (such as linguistic hedging and swearing). Here, we introduce a new computational framework for studying expressions of the intimacy in language with an accompanying dataset and deep learning model for accurately predicting the intimacy level of questions (Pearson r = 0.87). Through analyzing a dataset of 80.5M questions across social media, books, and films, we show that individuals employ interpersonal pragmatic moves in their language to align their intimacy with social settings. Then, in three studies, we further demonstrate how individuals modulate their intimacy to match social norms around gender, social distance, and audience, each validating key findings from studies in social psychology. Our work demonstrates that intimacy is a pervasive and impactful social dimension of language. 

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