The online social network Twitter, apart from being one of the main vehicles of communication in the cyberspace, has become an effective diffusor of fear of crime. The latter phenomenon has caught the attention of researchers since the 1960’s, amongst other reasons due to the impact on the citizens’ quality of life and consequently the call for its public management. Yet, the evaluation of fear of crime in the cyberspace, and more precisely on Twitter, is practically inexistent to the date.Based on a sample of tweets pertaining to three different hashtags (#prayforbarcelona, #stopislam, and #barcelona), which were gathered during the attacks on Barcelona in August 2017, our study pretends to investigate how users (n = 450) of Twitter perceive tweets to affect the public appraisal of security. These data were contrasted with a database of affective norms for more than 10,000 words in the Spanish language (Stadthagen-González, Ferré, Pérez-Sánchez, Imbault, & Hinojosa, 2017). We correlated the emotive values of tweets (based on their lexicon) with the estimations of our research participants. The results show significant correlations between various discrete basic emotions (fear, happiness, sadness) ) and our participants’ judgements. We achieved the same for one continuous emotional dimension (valence). This study shows, even though not conclusively, that the emotion transported via the linguistic material has an impact on the estimated likelihood of affecting the public perception of security when elicited in a space of potential crime, specifically in the cyberspace. Our results allow us to (1) continue along this kind of method, contrasting traditional methodology by approaching fear of crime through a combination of Big Data Analysis and linguistic emotion detection in written text. They furthermore allow us to (2) establish the methodological bases to design an automatized detector of fear of crime for Twitter, which we will attempt in a series of follow up studies. Our long-term goal is to program classifying algorithms to identify linguistic material with a high likelihood of affecting the public feeling of security.