Social media posts incorporate real-time information that has, elsewhere, been exploited to predict social trends. This paper considers whether such information can be useful in relation to crime and fear of crime. A large number of tweets were collected from the 18 largest Spanish-speaking countries in Latin America, over a period of 70 days. These tweets are then classified as being crime-related or not and additional information is extracted, including the type of crime and where possible, any geo-location at a city level. From the analysis of collected data, it is established that around 15 out of every 1000 tweets have text related to a crime, or fear of crime. The frequency of tweets related to crime is then compared against the number of murders, the murder rate, or the level of fear of crime as recorded in surveys. Results show that, like mass media, such as newspapers, social media suffer from a strong bias towards violent or sexual crimes. Furthermore, social media messages are not highly correlated with crime. Thus, social media is shown not to be highly useful for detecting trends in crime itself, but what they do demonstrate is rather a reflection of the level of the fear of crime.The use of social media completely revolutionised the way in which information is now shared and consumed, and is now a relevant part of government agencies and companies (Kaplan and Haenlein, 2010). Social media has given its users the ability to share content and opinions without having to depend on traditional and centralised news media outlets, potentially obtaining a more democratic distribution of opinions, offering users the ability to reach a large proportion of the population (Kwak et al., 2010).
Data collected from social media is a valuable input to analyse the flow of information, opinions and sentiments, and by detecting who shares what and how frequently. Millions (or perhaps even billions) of posts or tweets have been used to detect social media activism (Xu et al., 2014), to assist emergency responders (Avvenuti et al., 2016, 2018), to analyse the spread of a disease (Lampos and Cristianini, 2012), to detect the role of different users in the network (Martinez Teutle, 2010) and their behaviour (Cresci et al., 2020; Mazza et al., 2019), to quantify media coverage (Prieto Curiel et al., 2019), to provide indications for tourists (Barchiesi et al., 2015a, b; Cresci, 2014; Muntean et al., 2015), to detect road traffic (D’Andrea et al., 2015), exposure to cross-ideological contents (Himelboim et al., 2013), access to political information (Himelboim et al., 2013) and political participation (Ausserhofer and Maireder, 2013), perception on social phenomena such as migration flows (Coletto, 2017), and even to detect the popularity of different types of food (Amato, 2017) and to construct a real-time measure of happiness or hedonometer (Dodds et al., 2011). Although most of what is shared in social media are not news, nor posts related to public issues, it has nonetheless become, for some, one of the main sources of political information and news (Gil de Zúñiga et al., 2012).

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