Reflections on Java and Islam 1979-2010

Since the late 1970s, technological developments, especially in communications and transportation, have contributed to growth of new modes of social interaction and, at the same time, to the strengthening of social bonds in geographically dispersed social groups and communities in Yogyakarta. Increased educational opportunities and especially the development of the Islamic university system have contributed to the development of a Muslim middle class and with it a Muslim consumer culture. There has also been a significant shift in the ways in which relationships between culture (kebudayaan) and religion (agama) are conceptualised. Elements of Javanese Muslim tradition including prayer meals (slametan) that were formerly called agama are now more commonly referred to as kebudayaan. This shift reflects and has contributed to the diminution of sectarian conflict. This cultural strategy is not always successful. It does, however, provide a basis for proactive measures to counter sectarian violence. This is evident in “cultural” festivals supported by the kraton (palace) in response to attacks on cultural performance events by exclusivist religious groups.[Sejak dekade 1970an, kemajuan teknologi, utamanya dalam komunikasi dan transportasi, telah mengubah cara interaksi sosial masyarakat Yogyakarta dan, pada saat yang sama, semakin memperkokoh ikatan antar kelompok sosial. Semakin meningkatnya kesempatan belajar [sekolah], utamanya dengan berdirinya Universitas Islam, terbukti mendorong terciptanya kalangan ‘Muslim kelas menengah’ sekaligus konsumerisme yang melekat pada kalangan tersebut. Selain itu, terjadi pula perubahan konsep dalammenilai agama dan kebudayaan. Beberapa bentuk tradisi Islam Jawa, seperti slametan, yang dulunya dinilai sebagai ritual agama, saat ini hanya disebut sebagai kebudayaan. Perubahan ini menggambarkan dan bahkan berperan dalam meminimalisir konflik sektarian, seperti terlihat jelas dalam acara festival “budaya” yang diadakan kraton sebagai respon terhadap pembubaran pementasan budaya oleh kalangan Muslim ekslusif.

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.Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature

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

Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature.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.CRUE-CSIC agreementSpringer Natur

Counterpublic Discourses in Facebook Comment Sections — A Comparative Analysis of Norwegian, Swedish, and Danish (Social) Media Discourse on Islam

publishedVersio

Reviewing the purpose novel : reception, social reform, and the limits of persuasion in turn-of-the-century American fiction

“Reviewing the Purpose Novel” addresses the relationship between novels and public opinion by recovering an understudied category of nineteenth-century American fiction and analyzing the reception of four examples of the genre. Though narratives about how novels can change history are popular, my research indicates that there were far more barriers to a novel impacting public opinion than critics generally recognize. Drawing from a wide range of newspaper, magazine, and archival material, “Reviewing the Purpose Novel” constructs thoroughly contextualized accounts of each novel’s publication history and reception to show the ways that reviewers established generic expectations that limited the persuasive power an individual purpose novel was granted. Rather than locating the purpose novel’s disappearance in the modernist disdain for literature that engaged too explicitly with political or social issues or with the New Critical insistence that critics focus on aesthetic and formal qualities over social and historical ones, “Reviewing the Purpose Novel” offers a counter-narrative that engages mass and popular reading modes to show that by the turn of the century, reviewers, writers, and readers may have liked to imagine that a novel could effect concrete social change, but the models reviewers offered for reading the novels tended to limit or resist the persuasive process.Englis

A systematic survey of online data mining technology intended for law enforcement

As an increasing amount of crime takes on a digital aspect, law enforcement bodies must tackle an online environment generating huge volumes of data. With manual inspections becoming increasingly infeasible, law enforcement bodies are optimising online investigations through data-mining technologies. Such technologies must be well designed and rigorously grounded, yet no survey of the online data-mining literature exists which examines their techniques, applications and rigour. This article remedies this gap through a systematic mapping study describing online data-mining literature which visibly targets law enforcement applications, using evidence-based practices in survey making to produce a replicable analysis which can be methodologically examined for deficiencies

Towards a National Security Analysis Approach via Machine Learning and Social Media Analytics

Various severe threats at national and international level, such as health crises, radicalisation, or organised crime, have the potential of unbalancing a nation's stability. Such threats impact directly on elements linked to people's security, known in the literature as human security components. Protecting the citizens from such risks is the primary objective of the various organisations that have as their main objective the protection of the legitimacy, stability and security of the state. Given the importance of maintaining security and stability, governments across the globe have been developing a variety of strategies to diminish or negate the devastating effects of the aforementioned threats. Technological progress plays a pivotal role in the evolution of these strategies. Most recently, artificial intelligence has enabled the examination of large volumes of data and the creation of bespoke analytical tools that are able to perform complex tasks towards the analysis of multiple scenarios, tasks that would usually require significant amounts of human resources. Several research projects have already proposed and studied the use of artificial intelligence to analyse crucial problems that impact national security components, such as violence or ideology. However, the focus of all this prior research was examining isolated components. However, understanding national security issues requires studying and analysing a multitude of closely interrelated elements and constructing a holistic view of the problem. The work documented in this thesis aims at filling this gap. Its main contribution is the creation of a complete pipeline for constructing a big picture that helps understand national security problems. The proposed pipeline covers different stages and begins with the analysis of the unfolding event, which produces timely detection points that indicate that society might head toward a disruptive situation. Then, a further examination based on machine learning techniques enables the interpretation of an already confirmed crisis in terms of high-level national security concepts. Apart from using widely accepted national security theoretical constructions developed over years of social and political research, the second pillar of the approach is the modern computational paradigms, especially machine learning and its applications in natural language processing

Automatically Detecting the Resonance of Terrorist Movement Frames on the Web

The ever-increasing use of the internet by terrorist groups as a platform for the dissemination of radical, violent ideologies is well documented. The internet has, in this way, become a breeding ground for potential lone-wolf terrorists; that is, individuals who commit acts of terror inspired by the ideological rhetoric emitted by terrorist organizations. These individuals are characterized by their lack of formal affiliation with terror organizations, making them difficult to intercept with traditional intelligence techniques. The radicalization of individuals on the internet poses a considerable threat to law enforcement and national security officials. This new medium of radicalization, however, also presents new opportunities for the interdiction of lone wolf terrorism. This dissertation is an account of the development and evaluation of an information technology (IT) framework for detecting potentially radicalized individuals on social media sites and Web fora. Unifying Collective Action Framing Theory (CAFT) and a radicalization model of lone wolf terrorism, this dissertation analyzes a corpus of propaganda documents produced by several, radically different, terror organizations. This analysis provides the building blocks to define a knowledge model of terrorist ideological framing that is implemented as a Semantic Web Ontology. Using several techniques for ontology guided information extraction, the resultant ontology can be accurately processed from textual data sources. This dissertation subsequently defines several techniques that leverage the populated ontological representation for automatically identifying individuals who are potentially radicalized to one or more terrorist ideologies based on their postings on social media and other Web fora. The dissertation also discusses how the ontology can be queried using intuitive structured query languages to infer triggering events in the news. The prototype system is evaluated in the context of classification and is shown to provide state of the art results. The main outputs of this research are (1) an ontological model of terrorist ideologies (2) an information extraction framework capable of identifying and extracting terrorist ideologies from text, (3) a classification methodology for classifying Web content as resonating the ideology of one or more terrorist groups and (4) a methodology for rapidly identifying news content of relevance to one or more terrorist groups