# Making Sense of Language Signals for Monitoring Radicalization
## 2 From Radicalization Drivers to Radicalization Signals
[23] researched the detection of psychological within-person changes through mobilizing interactions in social media. For this purpose, they conducted a pilot with a sample of Hong Kong residents during the violent protests of 2019. The detection of mobilizing interactions was based on the analysis of short texts written by the participants describing the political situation to detect collective action intentions (e.g., \"I intend to encourage the family to sign a petition to stand up for the cause\"). The _driver_ of this radicalization is a socially grounded grievance that urges us to change the unjust situation via collective actions. To detect this grievance feeling, the authors detected emotional reactions. In particular, they used the LIWC dictionary [9] to detect anger and the Moral Foundations Theory (MFT) [24] dictionary to detect harm. Their results conclude that communication, including harm, threat, and anger, was mobilizing and increasing support for the undertaking of collective actions. Based on these results, they conducted a significant study to research engaging online interactions on Twitter to support the extremist group Daesh. They detected 65 features associated with expressing support for Daesh. In addition to LIWC and MFT, they use a vernacular Daesh dictionary since the use of ingroup jargon is a relevant sign of group identity. In order to understand the radicalization process whose signal is within-person changes in linguistic style, they carried out a mesoanalysis considering aspects such as the number of sequential LIWC accounts, followers, and following. They concluded that changes in the linguistic style and vernacular word use are associated with engaging in mobilizing interactions. This enables the detection of radicalization processes. Nevertheless, they did not disclose the feature set or annotated datasets with narratives for ethical reasons, since there is not yet a gold standard to evaluate its generality. [PERSON] et al. [25] analyzed the difference in the language used between pro-ISIS users and random users. Their findings are that ISIS supporters use more third-person plural pronouns and fewer first and second-person pronouns. In addition, they use more words related to death, certainty, and anger. They use the LIWC [9] dictionary. [PERSON] et al. [26] analyzed if psychological and moral variables exist that correlate with political orientation and extremity. For this purpose, they analyzed Twitter accounts of both neo-Nazis as right-wing extremists and Antifa as left-wing extremists. Their study included a validation of the LIWC [9] and MFT [24] dictionaries for extremist-written tweets. They concluded that left-wing extremists express some language indicating anxiety between the four political groups (liberals, conservatives, left-extremists, and right-extremists). In addition, right-wing extremists express more positive emotions and fewer negative emotions than left-wing extremists. Regarding the MFT, right-wing extremists use language more indicative of obedience to authority and purity and more language indicative of fairness and harm avoidance than left-wing extremists. [PERSON] et al. [27] proposed identifying several vulnerability factors based on text analysis. They analyzed two personality factors, frustration and introversion, and three attitudes and beliefs towards the Muslim religion and Western society. The detection of frustration was based on swear words and sentences with a negative connotation. Regarding introversion, the proposal was to leverage the sentence length and the use of ellipses. Finally, they used keywords to count the perception of being discriminatedagainst for being Muslim, negative opinions about Western society, and positive ideas about jihadism. Several recent language resources provide new possibilities for understanding better extremist language. The Grievance Dictionary [28] is a psycholinguistic dictionary for grievance-fueled threat assessment.