For more than a decade now, academicians and online platform administrators have been studying solutions to the problem of bot detection. Bots are computer algorithms whose use is far from being benign: malicious bots are purposely created to distribute spam, sponsor public characters and, ultimately, induce a bias within the public opinion. To fight the bot invasion on our online ecosystem, several approaches have been implemented, mostly based on (supervised and unsupervised) classifiers, which adopt the most varied account features, from the simplest to the most expensive ones to be extracted from the raw data obtainable through the Twitter public APIs. In this exploratory study, using Twitter as a benchmark, we compare the performances of four state-of-art feature sets in detecting novel bots: one of the output scores of the popular bot detector Botometer, which considers more than 1,000 features of an account to take a decision; two feature sets based on the account profile and timeline; and the information about the Twitter client from which the user tweets. The results of our analysis, conducted on six recently released datasets of Twitter accounts, hint at the possible use of general-purpose classifiers and cheap-to-compute account features for the detection of evolved bots.

Social networks form a valuable source of world knowledge, where influential entities correspond to popular accounts. Unlike factual knowledge bases (KBs), which maintain a semantic ontology, structured semantic information is not available on social media. In this work, we consider a social KB of roughly 200K popular Twitter accounts, which denotes entities of interest. We elicit semantic information about those entities. In particular, we associate them with a fine-grained set of 136 semantic types, e.g., determine whether a given entity account belongs to a politician, or a musical artist. In the lack of explicit type information in Twitter, we obtain semantic labels for a subset of the accounts via alignment with the KBs of DBpedia and Wikidata. Given the labeled dataset, we finetune a transformer-based text encoder to generate semantic embeddings of the entities based on the contents of their accounts. We then exploit this evidence alongside network-based embeddings to predict the entities semantic types. In our experiments, we show high type prediction performance on the labeled dataset. Consequently, we apply our type classification model to all of the entity accounts in the social KB. Our analysis of the results offers insights about the global semantics of the Twitter sphere. We discuss downstream applications that should benefit from semantic type information and the semantic embeddings of social entities generated in this work. In particular, we demonstrate enhanced performance on the key task of entity similarity assessment using this information.

While the finer points of running a social media business can be debated, one basic truth is that they all run on attention. Tech leaders are incentivized to grow their user bases so there are more people looking at more ads for more time. As the owner of Twitter, Elon Musk presumably shared that goal. But he claimed he hadn't bought Twitter to make money. This freed him up to focus on other passions: stopping rival tech companies from scraping Twitter's data without permission--even if it meant losing eyeballs on ads.

While numerous public blockchain datasets are available, their utility is constrained by a singular focus on blockchain data. This constraint limits the incorporation of relevant social network data into blockchain analysis, thereby diminishing the breadth and depth of insight that can be derived. To address the above limitation, we introduce ETGraph, a novel dataset that authentically links Ethereum and Twitter, marking the first and largest dataset of its kind. ETGraph combines Ethereum transaction records (2 million nodes and 30 million edges) and Twitter following data (1 million nodes and 3 million edges), bonding 30,667 Ethereum addresses with verified Twitter accounts sourced from OpenSea. Detailed statistical analysis on ETGraph highlights the structural differences between Twitter-matched and non-Twitter-matched Ethereum addresses. Extensive experiments, including Ethereum link prediction, wash-trading Ethereum addresses detection, and Twitter-Ethereum matching link prediction, emphasize the significant role of Twitter data in enhancing Ethereum analysis. ETGraph is available at https://etgraph.deno.dev/.

Misinformation is a global concern and limiting its spread is critical for protecting democracy, public health, and consumers. We propose that consumers' own social media post-histories are an underutilized data source to study what leads them to share links to fake-news. In Study 1, we explore how textual cues extracted from post-histories distinguish fake-news sharers from random social media users and others in the misinformation ecosystem. Among other results, we find across two datasets that fake-news sharers use more words related to anger, religion and power. In Study 2, we show that adding textual cues from post-histories improves the accuracy of models to predict who is likely to share fake-news. In Study 3, we provide a preliminary test of two mitigation strategies deduced from Study 1 - activating religious values and reducing anger - and find that they reduce fake-news sharing and sharing more generally. In Study 4, we combine survey responses with users' verified Twitter post-histories and show that using empowering language in a fact-checking browser extension ad increases download intentions. Our research encourages marketers, misinformation scholars, and practitioners to use post-histories to develop theories and test interventions to reduce the spread of misinformation.

Private video calls, text messages and meetings on Zoom might be used to'train' artificial intelligence models. The San Jose company's new terms and conditions - which came into force in March but were spotted this month - have sparked a wave of outrage online, with users threatening to cancel their accounts over the change. In one section of the new T C's, it says that customers consent to Zoom using data for purposes such as'machine learning or artificial intelligence (including for the purposes of training and tuning of algorithms and models).' Artificial intelligence models are commonly trained with large amounts of publicly available data, often taken from the internet - but Zoom's move would use private customer data, raising privacy fears. The changes came in paragraph 10.4 of Zoom's Terms and Conditions (Zoom) Zoom has responded with a blog post this week, claiming that the data is only used to train AI models to summarize meetings more accurately, and only with customer consent. In a blog post, Zoom's Chief Product Officer Smita Hashim wrote: 'To reiterate: we do not use audio, video, or chat content for training our models without customer consent.'

A suspected AI-generated image claiming to show an explosion near the Pentagon went viral on Twitter Monday, sending markets crashing. Dozens of verified accounts - including national news organizations - reshared what shows black smoke billowing up from the ground next to a white building. The image appears so realistic that people became frantic as it circulated the platform around 10 am ET, which caused the S&P 500 to drop 10 points in five minutes as the image went viral. The Arlington Fire Department swiftly debunked the event, stating that'there is no explosion or incident taking place at or near the Pentagon reservation.' It comes as fears about the power of artificial technology in spreading misinformation, particularly in the build-up to the 2024 Presidential Election.

An AI generated newsreader has been revealed by a media outlet in Kuwait. Kuwait News posted a video to their Twitter account over the weekend showing the computerised anchor introducing "herself" as "Fedha". The AI video showed a blonde woman wearing a black jacket and a white T-shirt. "I'm Fedha, the first presenter in Kuwait who works with artificial intelligence at Kuwait News. What kind of news do you prefer? Let's hear your opinions," she said in Arabic.

A Kuwaiti media outlet has unveiled a virtual news presenter generated using artificial intelligence, with plans for it to read online bulletins. "Fedha" appeared on the Twitter account of the Kuwait News website on Saturday as an image of a woman, hair uncovered, wearing a black jacket and white T-shirt. "I'm Fedha, the first presenter in Kuwait who works with artificial intelligence at Kuwait News. What kind of news do you prefer? Let's hear your opinions," she said in Arabic.

A Kuwaiti media outlet has unveiled a virtual news presenter generated using artificial intelligence, with plans for it to read online bulletins. "Fedha" appeared on the Twitter account of the Kuwait News website on Saturday as an image of a woman, her light-colored hair uncovered, wearing a black jacket and white T-shirt. "I'm Fedha, the first presenter in Kuwait who works with artificial intelligence at Kuwait News. What kind of news do you prefer? Let's hear your opinions," she said in classical Arabic.