# Making Sense of Language Signals for Monitoring Radicalization
## 2 From Radicalization Drivers to Radicalization Signals  
Social signals [3] are _\"communicative or informative signals which, directly or indirectly, provide information about social facts, that is, about social interactions, social attitudes, social relations, and social emotions.\"_ Humans produce social signals in diverse modalities [3], such as words, acoustic features, gestures, posture, head movements, facial expression, gaze, proxemics, touch, physical contact, and spacial behavior. In this work, we are explicitly interested in social signals, which can be derived from the texts posted on social networks and social interactions (e.g., reply, retweet, like, and so on). These signals are known as the _social context_, which be defined as _\"the collection of users, content, relations, and interactions which describe the environment in which social activity takes place\"_ [4]. When it comes to detecting radicalization signals in texts posted on social networks, many works have focused on analyzing, detecting, and predicting radicalization [5,6]. Online radicalization analysis aims at improving security agencies' decision processes by providing insights about the network structure (e.g., leaders and communities) and the posted contents (e.g., authorship identification and stylometric and affect analysis) [5]. Detection works [7] focus on detecting radicalization activity. For example, [PERSON] et al. [8] proposed a system that classifies tweets as Islamic State of Iraq and Syria (ISIS) supporters or not. For this purpose, they combined micro and macroanalysis using textual and social network features. The microanalysis consists of stylistic signals (extracted from the Dabiq magazine) and psychological, personality, and emotional signals, extracted with the Linguistic Inquiry and Word Count (LIWC) [9] dictionary. Meso analysis evaluates user activity, followers/following ratio, and the influence of users extracted from the mentioned interaction graph. [PERSON] et al. [6] analyzed the three roots of radicalization (micro, meso, and macro) to understand the radicalization pathway through the analysis of ISIS supporters' antecedents. First, they characterized the micro and meso levels as the posts created and shared by users. Second, they analyzed the shared web addresses in the posts at the macro level. Third, they proposed computing the influence of each level by calculating the similarity of these posts (created or shared) with lexicons of radical language. Finally, [PERSON] et al. [10] concluded that emotional signals are adequate for characterizing radical language. However, most works address analysis, detection, and prediction as a binary classification task that classifies individuals into radicals and not radicals. Instead, in this work, we are interested in identifying radicalization factors. [PERSON] et al. [11] suggested that radicalism and extremism can be considered a continuum. Based on the research desk, the authors provided a taxonomy of risk, protective, and mobilization factors. _Risk factors_ are thoughts or behaviors that have been present in radicalization pathways. Most of them are considered radicalization _drivers_, such as hardened points of view, marginalization, perceived discrimination, and connection to extremists. In contrast, _protective factors_ are those that reduce the impacts of risk factors and should be targeted in counter-radicalization policies. They include social connection, pluralistic inclusivity, resilience, emotional stability, and professional engagement. Finally, _ mobilization factors_ are those present when an individual transitions from a planned radicalization toward implementation. These factors are a direct threat, escalation to action, increased group pressure, and leakage. Several models have been defined to describe, identify, and counter the pathway to violent extremism [12], such as the Pathway to Violence Model [13] and the Warning Behaviors Model [14].