ChatGPT may well revolutionize web search, streamline office chores, and remake education, but the smooth-talking chatbot has also found work as a social media crypto huckster. Researchers at Indiana University Bloomington discovered a botnet powered by ChatGPT operating on X--the social network formerly known as Twitter--in May of this year. The botnet, which the researchers dub Fox8 because of its connection to cryptocurrency websites bearing some variation of the same name, consisted of 1,140 accounts. Many of them seemed to use ChatGPT to craft social media posts and to reply to each other's posts. The auto-generated content was apparently designed to lure unsuspecting humans into clicking links through to the crypto-hyping sites.

Christopher Bouzy is trying to stay ahead of the bots. As the person behind Bot Sentinel, a popular bot-detection system, he and his team continuously update their machine learning models out of fear that they will get "stale." The task? Sorting 3.2 million tweets from suspended accounts into two folders: "Bot" or "Not." To detect bots, Bot Sentinel's models must first learn what problematic behavior is through exposure to data. And by providing the model with tweets in two distinct categories--bot or not a bot--Bouzy's model can calibrate itself and allegedly find the very essence of what, he thinks, makes a tweet problematic.

The deluge of online and offline misinformation is overloading the exchange of ideas upon which democracies depend. Fake news, conspiracy theories, and deceptive social bots proliferate, facilitating the manipulation of public opinion. Countering misinformation while protecting freedom of speech will require collaboration across industry, journalism, and academia. The Workshop on Digital Misinformation — held in May 2017 in conjunction with the International Conference on Web and Social Media in Montréal, Québec, Canada — was intended to foster these efforts. The meeting brought together more than 100 stakeholders from academia, media, and tech companies to discuss the research challenges implicit in building a trustworthy Web. Below we outline the main findings from the discussion.

Collaborative query routing is a new paradigm for web search that treats both established search engines and other publicly available indexes as intelligent peer agents in a search network. The approach makes it transparent for anyone to build his or her own (micro) search engine by integrating established web search services, desktop search, and topical crawling techniques. The challenge in this model is that each of these agents must learn about its environment--the existence, knowledge, diversity, reliability, and trustworthiness of other agents--by analyzing the queries received from and results exchanged with these other agents. We present the 6S peer network, which uses machine-learning techniques to learn about the changing query environment. We show that simple reinforcement learning algorithms are sufficient to detect and exploit semantic locality in the network, resulting in efficient routing and highquality search results.

The pizzeria vowed on Monday to stay open despite a shooting incident sparked by a fake news report that it was fronting a child sex ring run by Democratic presidential candidate Hillary Clinton. On Sunday afternoon, a 28-year-old man walked into a Washington, D.C. ping-pong bar and pizzeria. He was carrying an AR-15 assault rifle – hardly standard-issue hardware for a round of table tennis. He fired one or more shots, as people fled Comet Ping Pong, before surrendering to police officers. Edgar Maddison Welch told police he had traveled from his home in Salisbury, N.C. to the nation's capital to investigate a pre-election conspiracy theory, wherein Democratic presidential nominee Hillary Clinton allegedly led a child-trafficking ring out of Comet Ping Pong.

Collaborative query routing is a new paradigm for Web search that treats both established search engines and other publicly available indices as intelligent peer agents in a search network. The approach makes it transparent for anyone to build their own (micro) search engine, by integrating established Web search services, desktop search, and topical crawling techniques. The challenge in this model is that each of these agents must learn about its environment— the existence, knowledge, diversity, reliability, and trustworthiness of other agents — by analyzing the queries received from and results exchanged with these other agents. We present the 6S peer network, which uses machine learning techniques to learn about the changing query environment. We show that simple reinforcement learning algorithms are sufficient to detect and exploit semantic locality in the network, resulting in efficient routing and high-quality search results. A prototype of 6S is available for public use and is intended to assist in the evaluation of different AI techniques employed by the networked agents.