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.

With the remarkable capability to reach the public instantly, social media has become integral in sharing scholarly articles to measure public response. Since spamming by bots on social media can steer the conversation and present a false public interest in given research, affecting policies impacting the public's lives in the real world, this topic warrants critical study and attention. We used the Altmetric dataset in combination with data collected through the Twitter Application Programming Interface (API) and the Botometer API. We combined the data into an extensive dataset with academic articles, several features from the article and a label indicating whether the article had excessive bot activity on Twitter or not. We analyzed the data to see the possibility of bot activity based on different characteristics of the article. We also trained machine-learning models using this dataset to identify possible bot activity in any given article. Our machine-learning models were capable of identifying possible bot activity in any academic article with an accuracy of 0.70. We also found that articles related to "Health and Human Science" are more prone to bot activity compared to other research areas. Without arguing the maliciousness of the bot activity, our work presents a tool to identify the presence of bot activity in the dissemination of an academic article and creates a baseline for future research in this direction.

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.

Alan Turing's famous test of whether machines could fool us into believing they were human – "the imitation game" – has become a mundane, daily question for all of us. We are surrounded by machine voices, and think nothing of conversing with them – though each time I hear my car tell me where to turn left I am reminded of my grandmother, who having installed a telephone late in life used to routinely say goodnight to the speaking clock. We find ourselves locked into interminable text chats with breezy automated bank tellers and offer our mother's maiden name to a variety of robotic speakers that sound plausibly alive. I've resisted the domestic spies of Apple and Amazon, but one or two friends jokingly describe the rapport they and their kids have built up with Amazon's Alexa or Google's Home Hub – and they are right about that: the more you tell your virtual valet, the more you disclose of wants and desires, the more speedily it can learn and commit to memory those last few fragments of your inner life you had kept to yourself. As the line between human and digital voices blurs, our suspicions are raised: who exactly are we talking to? No online conversation or message-board spat is complete without its doubters: "Are you a bot?" Or, the contemporary door-slam: "Bot: blocked!" Those doubts will only increase. The ability of bots – a term which can describe any automated process present in a computer network – to mimic human online behaviour and language has developed sharply in the past three years.