A delivery driver posted a TikTok alleging she had been sexually assaulted by a customer. The deepfakes that followed reveal a growing digital blackface problem. When DoorDash delivery driver Livie Rose Henderson posted a video alleging that one of her customers sexually assaulted her in October, it set off a firestorm of reactions. Henderson's TikTok claimed that when she was dropping off a delivery in Oswego, New York, she found a customer's front door wide open and inside, a man on the couch with his pants and underwear pulled down to his ankles. Henderson was dubbed the "DoorDash Girl," and her video accrued tens of millions of views, including some supportive and consoling responses to what she said she had endured on the job as a young woman.

Social bots remain a major vector for spreading disinformation on social media and a menace to the public. Despite the progress made in developing multiple sophisticated social bot detection algorithms and tools, bot detection remains a challenging, unsolved problem that is fraught with uncertainty due to the heterogeneity of bot behaviors, training data, and detection algorithms. Detection models often disagree on whether to label the same account as bot or human-controlled. However, they do not provide any measure of uncertainty to indicate how much we should trust their results. We propose to address both bot detection and the quantification of uncertainty at the account level -- a novel feature of this research. This dual focus is crucial as it allows us to leverage additional information related to the quantified uncertainty of each prediction, thereby enhancing decision-making and improving the reliability of bot classifications. Specifically, our approach facilitates targeted interventions for bots when predictions are made with high confidence and suggests caution (e.g., gathering more data) when predictions are uncertain.

T erence Broad Creative Computing Institute University of the Arts London United Kingdom t.broad@arts.ac.uk Abstract The dead internet theory is a conspiracy theory that states that all interactions and posts on social media are no longer being made by real people, but rather by autonomous bots. While the theory is obviously not true, an increasing amount of posts on social media have been made by bots optimised to gain followers and drive engagement on social media platforms. This paper looks at the recent phenomenon of these bots, analysing their behaviour through the lens of computational creativity to investigate the question: is computational creativity flourishing on the dead internet? Introduction The dead internet theory is a conspiracy theory that emerged in the late 2010's or early 2020's that states that large parts of the internet, in particular on social media are no longer occupied by humans and human generated content, but rather posts by AI-driven bots that are designed to control or influence human behaviour (IlluminatiPirate 2021). Whist the theory emerges from the fringes of the internet, stemming in conspiratorial thinking as a way of explaining broad-based changes to society from nefarious actors, many commentators have observed that there is a grain of truth to the theory (Tiffany 2021).

The emergence of Large Language Models (LLMs) has great potential to reshape the landscape of many social media platforms. While this can bring promising opportunities, it also raises many threats, such as biases and privacy concerns, and may contribute to the spread of propaganda by malicious actors. We developed the "LLMs Among Us" experimental framework on top of the Mastodon social media platform for bot and human participants to communicate without knowing the ratio or nature of bot and human participants. We built 10 personas with three different LLMs, GPT-4, LLama 2 Chat, and Claude. We conducted three rounds of the experiment and surveyed participants after each round to measure the ability of LLMs to pose as human participants without human detection. We found that participants correctly identified the nature of other users in the experiment only 42% of the time despite knowing the presence of both bots and humans. We also found that the choice of persona had substantially more impact on human perception than the choice of mainstream LLMs.

Social media bot detection has always been an arms race between advancements in machine learning bot detectors and adversarial bot strategies to evade detection. In this work, we bring the arms race to the next level by investigating the opportunities and risks of state-of-the-art large language models (LLMs) in social bot detection. To investigate the opportunities, we design novel LLM-based bot detectors by proposing a mixture-of-heterogeneous-experts framework to divide and conquer diverse user information modalities. To illuminate the risks, we explore the possibility of LLM-guided manipulation of user textual and structured information to evade detection. Extensive experiments with three LLMs on two datasets demonstrate that instruction tuning on merely 1,000 annotated examples produces specialized LLMs that outperform state-of-the-art baselines by up to 9.1% on both datasets, while LLM-guided manipulation strategies could significantly bring down the performance of existing bot detectors by up to 29.6% and harm the calibration and reliability of bot detection systems.

Twitter as one of the most popular social networks, offers a means for communication and online discourse, which unfortunately has been the target of bots and fake accounts, leading to the manipulation and spreading of false information. Towards this end, we gather a challenging, multilingual dataset of social discourse on Twitter, originating from 9M users regarding the recent Russo-Ukrainian war, in order to detect the bot accounts and the conversation involving them. We collect the ground truth for our dataset through the Twitter API suspended accounts collection, containing approximately 343K of bot accounts and 8M of normal users. Additionally, we use a dataset provided by Botometer-V3 with 1,777 Varol, 483 German accounts, and 1,321 US accounts. Besides the publicly available datasets, we also manage to collect 2 independent datasets around popular discussion topics of the 2022 energy crisis and the 2022 conspiracy discussions. Both of the datasets were labeled according to the Twitter suspension mechanism. We build a novel ML model for bot detection using the state-of-the-art XGBoost model. We combine the model with a high volume of labeled tweets according to the Twitter suspension mechanism ground truth. This requires a limited set of profile features allowing labeling of the dataset in different time periods from the collection, as it is independent of the Twitter API. In comparison with Botometer our methodology achieves an average 11% higher ROC-AUC score over two real-case scenario datasets.

Social coding platforms have revolutionized collaboration in software development, leading to using software bots for streamlining operations. However, The presence of open-source software (OSS) bots gives rise to problems including impersonation, spamming, bias, and security risks. Identifying bot accounts and behavior is a challenging task in the OSS project. This research aims to investigate bots' behavior in open-source software projects and identify bot accounts with maximum possible accuracy. Our team gathered a dataset of 19,779 accounts that meet standardized criteria to enable future research on bots in open-source projects. We follow a rigorous workflow to ensure that the data we collect is accurate, generalizable, scalable, and up-to-date. We've identified four types of bot accounts in open-source software projects by analyzing their behavior across 17 features in 5 dimensions. Our team created BotHawk, a highly effective model for detecting bots in open-source software projects. It outperforms other models, achieving an AUC of 0.947 and an F1-score of 0.89. BotHawk can detect a wider variety of bots, including CI/CD and scanning bots. Furthermore, we find that the number of followers, number of repositories, and tags contain the most relevant features to identify the account type.

An essential topic in online social network security is how to accurately detect bot accounts and relieve their harmful impacts (e.g., misinformation, rumor, and spam) on genuine users. Based on a real-world data set, we construct behavioral sequences from raw event logs. After extracting critical characteristics from behavioral time series, we observe differences between bots and genuine users and similar patterns among bot accounts. We present a novel social bot detection system BotShape, to automatically catch behavioral sequences and characteristics as features for classifiers to detect bots. We evaluate the detection performance of our system in ground-truth instances, showing an average accuracy of 98.52% and an average f1-score of 96.65% on various types of classifiers. After comparing it with other research, we conclude that BotShape is a novel approach to profiling an account, which could improve performance for most methods by providing significant behavioral features.

A recent Washington Post article about artificial intelligence (AI) briefly caught the publics' attention. A former engineer working for Google's Responsible AI organization went public with his belief that the company's chatbot was sentient. It should be stated bluntly: this AI is not a conscious entity. It is a large language model trained indiscriminately from Internet text that uses statistical patterns to predict the most probable sequence of words. While the tone of the Washington Post piece conjured all the usual Hollywood tropes related to humanity's fear of sentient technology (e.g., storylines from Ex Machina, Terminator, or 2001: A Space Odyssey), it also inadvertently highlighted an uncomfortable truth: As AI capabilities continue to improve, they will become increasingly effective tools for manipulating and fooling humans.

This article is part of Demystifying AI, a series of posts that (try to) disambiguate the jargon and myths surrounding AI. Graphs are everywhere around us. Your social network is a graph of people and relations. The roads you take to go from point A to point B constitute a graph. The links that connect this webpage to others form a graph.