The spread of misinformation online, most commonly known as fake news, is an important issue that has become more pronounced in the last two decades due to the prevalence of social media. Platforms like Twitter, Reddit, and Facebook, have been commonly identified as the main channels for propagating misinformation and have been criticized for not acting on addressing the conditions that permit the circulation and amplification of false information [32]. Such misinformation includes false claims and non fact-checked news items, that originate from sources of questionable credibility [113]. The problem of misinformation becomes critical when it pertains to healthcare and health issues, since it puts lives and the public health at risk. One of the first cases of widely spread misinformation in the medical domain is the falsehood that the MMR vaccine (Measles, Mumps, Rubella) causes autism [109]. The falsehood originated from a fraudulent article titled "Ileal-lymphoid-nodular hyperplasia, non-specific colitis, and pervasive developmental disorder in children" published in the prestigious Lancet journal in 1998 [171, 197]. This study turned tens of thousands of parents against the vaccine, and as a result, in 2020, many countries, including the United Kingdom, Greece, Venezuela, and Brazil, lost their measles elimination status. In 2020, twenty-two years after publishing this study Lancet retracted the paper [203].

Location-based social sites, such as Foursquare or Yelp, are gaining increasing popularity. These sites allow users to check in at venues and leave a short commentary in the form of a micro-review. Micro-reviews are rich in content as they offer a distilled and concise account of user experience. In this paper we consider the problem of predicting the topic of a micro-review by a user who visits a new venue. Such a prediction can help users make informed decisions, and also help venue owners personalize users’ experiences. However, topic modeling for micro-reviews is particularly difficult, due to their short and fragmented nature. We address this issue using pooling strategies, which aggregate micro-reviews at the venue or user level, and we propose novel probabilistic models based on Latent Dirichlet Allocation (LDA) for extracting the topics related to a user-venue pair. Our best topic model integrates influences from both venue inherent properties and user preferences, considering at the same the sentiment orientation of the users. Experimental results on real datasets demonstrate the superiority of this model compared to simpler models and previous work; they also show that venue-inherent properties have higher influences on the topics of micro-reviews.