Detecting psychological change through mobilizing interactions and changes in extremist linguistic style

The Intergroup Value Protection Model: A Theoretically Integrative and Dynamic Approach to Intergroup Conflict Escalation in Democratic Societies

SCIENTIFIC ABSTRACT

We review social-psychological evidence for a theoretically integrative and dynamic model of intergroup conflict escalation within democratic societies. Viewing individuals as social regulators who protect their social embeddedness (e.g., in their group or in society), the intergroup value protection model (IVPM) integrates key insights and concepts from moral and group psychology (e.g., group identification, outrage, moralization, protest) into a functional intergroup value protection process. The model assumes that social regulators are continuously looking for information diagnostic of the outgroup's intentions to terminate the relationship with the ingroup, and that their specific cognitive interpretations of an outgroup's action (i.e., as a violation of ingroup or shared values) trigger this process. The visible value-protective responses of one group can trigger the other group's value-protective responses, thus dynamically increasing chances of conflict escalation. We discuss scientific implications of integrating moral and group psychology and practical challenges for managing intergroup conflict within democratic societies.

PUBLIC ABSTRACT

The 2021 Capitol Hill attack exemplifies a major "trigger event" for different groups to protect their values within a democratic society. Which specific perceptions generate such a triggering event, which value-protective responses does it trigger, and do such responses escalate intergroup conflict? We offer the intergroup value protection model to analyze the moral and group psychology of intergroup conflict escalation in democratic societies. It predicts that when group members cognitively interpret another group's actions as violating ingroup or shared values, this triggers the intergroup value protection process (e.g., increased ingroup identification, outrage, moralization, social protest). When such value-protective responses are visible to the outgroup, this can in turn constitute a trigger event for them to protect their values, thus increasing chances of intergroup conflict escalation. We discuss scientific implications and practical challenges for managing intergroup value conflict in democratic societies, including fears of societal breakdown and scope for social change.

Terrorism and the internet: How dangerous is online radicalization?

This work is concerned with the extent and magnitude of threat related to online radicalization in the context of terrorist acts and related offending. Online influences have been depicted as major drivers for the propagation and adoption of extremist ideologies, which often contain an element of collective grievance, and subsequent acts of violence. This is most pronounced in the discussion of so-called lone actor terrorism, but extends to all forms of extremist offending, and beyond. The present work situates online radicalization leading to terrorist acts within the wider context of grievance-based beliefs and attitudes. Further, it addresses current positions and debates surrounding the relevance and mechanisms of online radicalization in terrorist offending. Recent evidence from quantitative studies is reviewed to estimate prevalence of online radicalization and the level of threat that results from it. This is followed by a discussion of plausible, but opposing, interpretations of the estimates presented. While online radicalization does occur, with and without reference to offline processes, the resulting threat is not overly high. This assessment, however, refers to the present only and is unlikely to hold for the future, given the general growth and acceleration of online activity among terrorist actors.

Detection of changes in literary writing style using N-grams as style markers and supervised machine learning

The analysis of an author’s writing style implies the characterization and identification of the style in terms of a set of features commonly called linguistic features. The analysis can be extrinsic, where the style of an author can be compared with other authors, or intrinsic, where the style of an author is identified through different stages of his life. Intrinsic analysis has been used, for example, to detect mental illness and the effects of aging. A key element of the analysis is the style markers used to model the author’s writing patterns. The style markers should handle diachronic changes and be thematic independent. One of the most commonly used style marker in extrinsic style analysis is n-gram. In this paper, we present the evaluation of traditional n-grams (words and characters) and dependency tree syntactic n-grams to solve the task of detecting changes in writing style over time. Our corpus consisted of novels by eleven English-speaking authors. The novels of each author were organized chronologically from the oldest to the most recent work according to the date of publication. Subsequently, two stages were defined: initial and final. In each stage three novels were assigned, novels of the initial stage corresponded to the oldest and those at the final stage to the most recent novels. To analyze changes in the writing style, novels were characterized by using four types of n-grams: characters, words, Part-Of-Speech (POS) tags and syntactic relations n-grams. Experiments were performed with a Logistic Regression classifier. Dimension reduction techniques such as Principal Component Analysis (PCA) and Latent Semantic Analysis (LSA) algorithms were evaluated. The results obtained with the different n-grams indicated that all authors presented significant changes in writing style over time. In addition, representations using n-grams of syntactic relations have achieved competitive results among different authors.

A survey on extremism analysis using natural language processing: definitions, literature review, trends and challenges

Extremism has grown as a global problem for society in recent years, especially after the apparition of movements such as jihadism. This and other extremist groups have taken advantage of different approaches, such as the use of Social Media, to spread their ideology, promote their acts and recruit followers. The extremist discourse, therefore, is reflected on the language used by these groups. Natural language processing (NLP) provides a way of detecting this type of content, and several authors make use of it to describe and discriminate the discourse held by these groups, with the final objective of detecting and preventing its spread. Following this approach, this survey aims to review the contributions of NLP to the field of extremism research, providing the reader with a comprehensive picture of the state of the art of this research area. The content includes a first conceptualization of the term extremism, the elements that compose an extremist discourse and the differences with other terms. After that, a review description and comparison of the frequently used NLP techniques is presented, including how they were applied, the insights they provided, the most frequently used NLP software tools, descriptive and classification applications, and the availability of datasets and data sources for research. Finally, research questions are approached and answered with highlights from the review, while future trends, challenges and directions derived from these highlights are suggested towards stimulating further research in this exciting research area.

The Grievance Dictionary: Understanding threatening language use

This paper introduces the Grievance Dictionary, a psycholinguistic dictionary that can be used to automatically understand language use in the context of grievance-fueled violence threat assessment. We describe the development of the dictionary, which was informed by suggestions from experienced threat assessment practitioners. These suggestions and subsequent human and computational word list generation resulted in a dictionary of 20,502 words annotated by 2318 participants. The dictionary was validated by applying it to texts written by violent and non-violent individuals, showing strong evidence for a difference between populations in several dictionary categories. Further classification tasks showed promising performance, but future improvements are still needed. Finally, we provide instructions and suggestions for the use of the Grievance Dictionary by security professionals and (violence) researchers.