Automatic detection of online hate speech serves as a crucial step in the detoxification of the online discourse. Moreover, accurate classification can promote a better understanding of the proliferation of hate as a social phenomenon. While most prior work focus on the detection of hateful utterances, we argue that focusing on the user level is as important, albeit challenging. In this paper we consider a multimodal aggregative approach for the detection of hate-mongers, taking into account the potentially hateful texts, user activity, and the user network. Evaluating our method on three unique datasets X (Twitter), Gab, and Parler we show that processing a user's texts in her social context significantly improves the detection of hate mongers, compared to previously used text and graph-based methods. We offer comprehensive set of results obtained in different experimental settings as well as qualitative analysis of illustrative cases. Our method can be used to improve the classification of coded messages, dog-whistling, and racial gas-lighting, as well as to inform intervention measures. Moreover, we demonstrate that our multimodal approach performs well across very different content platforms and over large datasets and networks.

Automatic detection of online hate speech serves as a crucial step in the detoxification of the online discourse. Moreover, accurate classification can promote a better understanding of the proliferation of hate as a social phenomenon. While most prior work focus on the detection of hateful utterances, we argue that focusing on the user level is as important, albeit challenging. In this paper we consider a multimodal aggregative approach for the detection of hate-mongers, taking into account the potentially hateful texts, user activity, and the user network. We evaluate our methods on three unique datasets X (Twitter), Gab, and Parler showing that a processing a user's texts in her social context significantly improves the detection of hate mongers, compared to previously used text and graph-based methods. Our method can be then used to improve the classification of coded messages, dog-whistling, and racial gas-lighting, as well as inform intervention measures. Moreover, our approach is highly efficient even for very large datasets and networks.

Online social networks have become a fundamental component of our everyday life. Unfortunately, these platforms are also a stage for hate speech. Popular social networks have regularized rules against hate speech. Consequently, social networks like Parler and Gab advocating and claiming to be free speech platforms have evolved. These platforms have become a district for hate speech against diverse targets. We present in our paper a pipeline for detecting hate speech and its targets and use it for creating Parler hate targets' distribution. The pipeline consists of two models; one for hate speech detection and the second for target classification, both based on BERT with Back-Translation and data pre-processing for improved results. The source code used in this work, as well as other relevant sources, are available at: https://github.com/NadavSc/HateRecognition.git

Coordinated disinformation campaigns are used to influence social media users, potentially leading to offline violence. In this study, we introduce a general methodology to uncover coordinated messaging through analysis of user parleys on Parler. The proposed method constructs a user-to-user coordination network graph induced by a user-to-text graph and a text-to-text similarity graph. The text-to-text graph is constructed based on the textual similarity of Parler posts. We study three influential groups of users in the 6 January 2020 Capitol riots and detect networks of coordinated user clusters that are all posting similar textual content in support of different disinformation narratives related to the U.S. 2020 elections.

Will artificial intelligence take over the world? Sometimes it seems that way, for everyone has heard of AI models that can compose entire essays or generate realistic face images of people who don't exist or create images from text descriptions. But these AI outcomes don't come cheap: the AI model must be "trained" by recreating countless permutations of those outcomes, and that training eats up enormous amounts of computer power on mammoth GPUs. XLNet from Google, for example, can cost around $61,000 to train each time, without guaranteed results. Not all AI models are as complicated as the Google one cited above.

One of Parler's first moves to try to get back online was to approach Amazon, according to former CEO John Matze, who was fired in February. He offered to explore using the Amazon's Rekognition AI tool, which reads faces, objects and scenes in images and videos and is used for content moderation by some of its customers. Amazon's own Trust & Safety team, which has fewer than 100 workers, acts only on complaints received and did receive complaints about Parler. But according to Matze, Amazon said implementing that tool wouldn't be enough to fix Parler's problem.

When hackers exploited a bug in Parler to download all of the right-wing social media platform's contents last week, they were surprised to find that many of the pictures and videos contained geolocation metadata revealing exactly how many of the site's users had taken part in the invasion of the US Capitol building just days before. But the videos uploaded to Parler also contain an equally sensitive bounty of data sitting in plain sight: thousands of images of unmasked faces, many of whom participated in the Capitol riot. Now one website has done the work of cataloging and publishing every one of those faces in a single, easy-to-browse lineup. Late last week, a website called Faces of the Riot appeared online, showing nothing but a vast grid of more than 6,000 images of faces, each one tagged only with a string of characters associated with the Parler video in which it appeared. The site's creator tells WIRED that he used simple open source machine learning and facial recognition software to detect, extract, and deduplicate every face from the 827 videos that were posted to Parler from inside and outside the Capitol building on January 6, the day when radicalized Trump supporters stormed the building in a riot that resulted in five people's deaths.

DuckDuckGo, a search engine focused on privacy, increased its average number of daily searches by 62% in 2020 as users seek alternatives to impede data tracking. The search engine, founded in 2008, operated nearly 23.7 billion search queries on their platform in 2020, according to their traffic page. On Jan. 11, DuckDuckGo reached its highest number of search queries in one day, with a total of 102,251,307. DuckDuckGo does not track user searches or share personal data with third-party companies. "People are coming to us because they want more privacy, and it's generally spreading through word of mouth," Kamyl Bazbaz, DuckDuckGo vice president of communications, told USA TODAY.

DuckDuckGo, a search engine focused on privacy, increased its average number of daily searches by 62% in 2020 as users seek alternatives to impede data tracking. The search engine, founded in 2008, operated nearly 23.7 billion search queries on their platform in 2020, according to their traffic page. On Jan. 11, DuckDuckGo reached its highest number of search queries in one day, with a total of 102,251,307. DuckDuckGo does not track user searches or share personal data with third-party companies. "People are coming to us because they want more privacy, and it's generally spreading through word-of-mouth," Kamyl Bazbaz, DuckDuckGo vice president of communications, told USA TODAY.