Scalable System for Opinion Mining on Twitter Data. Dynamic Visualization for Data Related to Refugees’ Crisis and to Terrorist Attacks

Social networks such as Twitter or Facebook grew rapidly in popularity, and users use them to share opinions about topics of interest, to be part of the community or to post messages that are available everywhere. This paper presents a system created in order to process streamed data taken from Twitter and classify it into positive, negative or neutral. The results of these processing’s can be visualized in a suggestive manner on Google Maps, users can select the language of the tweets, can group tweets that present the same news and can even display a dynamic evolution of the news in terms of its appearance. With all this amount of information it is very opportune to do some data analysis to detect different types of events (and their locations) that happen worldwide, especially at the time when this data represents information related to refugee crisis or signals terrorist attacks

Solutions to Detect and Analyze Online Radicalization : A Survey

Online Radicalization (also called Cyber-Terrorism or Extremism or Cyber-Racism or Cyber- Hate) is widespread and has become a major and growing concern to the society, governments and law enforcement agencies around the world. Research shows that various platforms on the Internet (low barrier to publish content, allows anonymity, provides exposure to millions of users and a potential of a very quick and widespread diffusion of message) such as YouTube (a popular video sharing website), Twitter (an online micro-blogging service), Facebook (a popular social networking website), online discussion forums and blogosphere are being misused for malicious intent. Such platforms are being used to form hate groups, racist communities, spread extremist agenda, incite anger or violence, promote radicalization, recruit members and create virtual organi- zations and communities. Automatic detection of online radicalization is a technically challenging problem because of the vast amount of the data, unstructured and noisy user-generated content, dynamically changing content and adversary behavior. There are several solutions proposed in the literature aiming to combat and counter cyber-hate and cyber-extremism. In this survey, we review solutions to detect and analyze online radicalization. We review 40 papers published at 12 venues from June 2003 to November 2011. We present a novel classification scheme to classify these papers. We analyze these techniques, perform trend analysis, discuss limitations of existing techniques and find out research gaps

A Framework for More Effective Dark Web Marketplace Investigations

The success of the Silk Road has prompted the growth of many Dark Web marketplaces. This exponential growth has provided criminal enterprises with new outlets to sell illicit items. Thus, the Dark Web has generated great interest from academics and governments who have sought to unveil the identities of participants in these highly lucrative, yet illegal, marketplaces. Traditional Web scraping methodologies and investigative techniques have proven to be inept at unmasking these marketplace participants. This research provides an analytical framework for automating Dark Web scraping and analysis with free tools found on the World Wide Web. Using a case study marketplace, we successfully tested a Web crawler, developed using AppleScript, to retrieve the account information for thousands of vendors and their respective marketplace listings. This paper clearly details why AppleScript was the most viable and efficient method for scraping Dark Web marketplaces. The results from our case study validate the efficacy of our proposed analytical framework, which has relevance for academics studying this growing phenomenon and for investigators examining criminal activity on the Dark Web

Semantic feature reduction and hybrid feature selection for clustering of Arabic Web pages

In the literature, high-dimensional data reduces the efficiency of clustering algorithms. Clustering the Arabic text is challenging because semantics of the text involves deep semantic processing. To overcome the problems, the feature selection and reduction methods have become essential to select and identify the appropriate features in reducing high-dimensional space. There is a need to develop a suitable design for feature selection and reduction methods that would result in a more relevant, meaningful and reduced representation of the Arabic texts to ease the clustering process. The research developed three different methods for analyzing the features of the Arabic Web text. The first method is based on hybrid feature selection that selects the informative term representation within the Arabic Web pages. It incorporates three different feature selection methods known as Chi-square, Mutual Information and Term Frequency–Inverse Document Frequency to build a hybrid model. The second method is a latent document vectorization method used to represent the documents as the probability distribution in the vector space. It overcomes the problems of high-dimension by reducing the dimensional space. To extract the best features, two document vectorizer methods have been implemented, known as the Bayesian vectorizer and semantic vectorizer. The third method is an Arabic semantic feature analysis used to improve the capability of the Arabic Web analysis. It ensures a good design for the clustering method to optimize clustering ability when analysing these Web pages. This is done by overcoming the problems of term representation, semantic modeling and dimensional reduction. Different experiments were carried out with k-means clustering on two different data sets. The methods provided solutions to reduce high-dimensional data and identify the semantic features shared between similar Arabic Web pages that are grouped together in one cluster. These pages were clustered according to the semantic similarities between them whereby they have a small Davies–Bouldin index and high accuracy. This study contributed to research in clustering algorithm by developing three methods to identify the most relevant features of the Arabic Web pages

Mapping Extremism: The Network Politics of the Far-Right

In recent decades, political parties espousing extreme nationalist, xenophobic, and even outright racist platforms have enjoyed variable success in national elections across Europe. While a vibrant research literature has sought to better understand the sources of support for such parties, remarkably little attention has been paid to the interplay between parties and the broader social networks of extremism in which they are embedded. To remedy this deficiency, the present study examines the relations between far-right parliamentary parties and their extra-parliamentary networks. One level of analysis tests whether there is a relationship between a party’s position within a network and its sustainability. Social network analysis is employed to assess the nature and structure of ties between Belgian organizations online. In addition, systematic textual analysis of website content is used to determine how a party’s ideological position within the network impacts its sustainability. The second level of analysis is a qualitative study based on in-depth interviews with members of Flemish nationalist organization in order to better understand how actors experience social networks. Evidence suggests that the most sustainable parties are those that have dense connections with other nationalist organizations. Mapping relations between far-right parties that compete openly within the rules of institutionalized democracy and their wider social networks can provide important policy-relevant insight into contemporary challenges posed by illiberal forces

Социальное пространство современного города

В коллективной монографии рассматриваются особенности функционирования и изменения социального пространства крупного промышленного центра, отношение его жителей к общественно-политическим, социокультурным процессам, которые в нем происходят в настоящее время. Используются материалы различных социологических исследований. Для научных работников, преподавателей вузов, руководителей муниципальных органов власти, студентов, а также всех интересующихся данной проблематикой

Out-group hate in the UK: Insights from race and religious hate crime representations and attitudes towards immigrants

Hate crimes have become a common problem in the United Kingdom (UK), especially following the European Union (EU) referendum and the BREXIT vote in June 2016. Consequently, hate crimes have received a great deal of attention in the recent past, with increasing discussions tailored around the need to accurately record and investigate these crimes. However, the field of hate crimes is complicated by inaccuracies in reporting and recording of these crimes, in addition to there being no clear understanding of how hate crimes are constructed by the general public and the intersection between the public perceptions and hate crime scholarship. Hate crime policing has advocated the protection of five-strands of people that are most likely to be recipients of such victimisation, while statistics suggest that two of the strands, race and religion, account for 80% of hate crime in the UK. Due to the frequent occurrence of race and religious hate crimes in the UK, this research aimed to investigate the general public and cultural perceptions and understandings of race and religious hate crime, in particular. Using a mixed-method design, this thesis conducted three empirical studies to investigate the facets of race and religious hate crimes in the UK. Study 1 carried out a cultural analysis of hate crime by examining newspaper articles to extract the key attributes evident in the reporting of race and religious hate crimes. A total of 22 key variables were seen to present when reporting such crimes, so for the general public these maybe the trigger for, and ideas by which, they come to define and understand an event as possibly being a hate crime. Study 2 looked more specifically at this perception and understanding of race and religious hate crimes amongst the general public by using a ‘storycompletion task’. The results suggested a variety of themes by which people might understand and demarcate race and religious hate crime; these are key social-psychological factors that need to be considered in terms of hate crime practice and policy. Finally, Study 3 evaluated the underlying social-psychological factors that may contribute to negative attitudes towards out-groups, a well-established finding in previous literature and evident in study two, that ‘othering’ and being seen as an out-group can be the basis of hate crime. The results suggested that people who are high on ethnocentrism are significantly more likely to show prejudice towards immigrants. In conclusion, the thesis highlighted that ‘othering’ individuals based on prejudicial attitudes can lead to hate crimes, therefore it is proposed that education on ethnic differences and early interventions to reduce prejudice (e.g. incorporating discussions of ethnicity in school curriculums), may be beneficial in reducing overall prejudice amongst the general public, which in-turn would help reduce hate crimes