Extremism Video Detection In Social Media

Social media has grown to become a fundamental part of our lives over the past two decades and with its growth, the misuse of the platform for extremist purposes has become common. The wide reach of social media has allowed extremist groups to take advantage of the platform to spread terrorist propaganda and fear. Therefore, the need for a robust extremist detector in social media is evident. As an attempt to combat this problem, we present techniques to detect various forms of extremism in videos crawled from Twitter, a social media to share short posts. We build upon existing deep neural networks used for action classification and create a model capable of recog- nizing certain common extremism types. Additionally, we also expand on logo/object detection models for the same purpose. We then use these models against a sample space of roughly 2 million unlabelled videos to test the accuracy of these models

The Majority Report - Can we use big data to secure a better future?

With the widely adopted use of social media, it now becomes a common platform for calling supporters for civil unrest events. Despite the noble aims of these civil unrest events, sometimes these events might turn violent and disturb the daily lives of the general public. This paper aims to propose a conceptual framework regarding the study of using online social media data to predict offline civil unrest events. We propose to use time-series metrics as the prediction attributes instead of analyzing message contents because the message contents on social media are usually noisy, informal and not so easy to interpret. In the case of a data set containing both civil unrest event dates and normal dates, we found that it contains many more samples from the normal dates class than from the civil unrest event dates class. Thus, creating an imbalanced class problem. We showed using accuracy as the performance metrics could be misleading as civil unrest events were the minority class. Thus, we suggest to use additional tactics to handle the imbalanced class prediction problem. We propose to use a combination of oversampling the minority class and using feature selection techniques to tackle the imbalanced class problem. The current results demonstrate that use of time-series metrics to predict civil unrest events is a possible solution to the problems of handling the noise and unstructured format of social media data contents in the process of analysis and predictions. In addition, we have showed that the combination of special techniques to handle imbalanced class outperformed other classifiers without using such techniques.published_or_final_versio

Features for Detecting Aggression in Social Media: An Exploratory Study

Cyberbullying and cyberaggression are serious and widespread issues increasingly affecting Internet users. With the “help" of the widespread of social media networks, bullying once limited to particular places or times of the day, can now occur anytime and anywhere. Cyberaggression refers to aggressive online behaviour intending to cause harm to another person, involving rude, insulting, offensive, teasing or demoralising comments through online social media. Considering the gravity of the consequences that cyberaggression has on its victims and its rapid spread amongst internet users (specially kids and teens), there is an imperious need for research aiming at understanding how cyberbullying occurs, in order to prevent it from escalating. Given the massive information overload on the Web, it is crucial to develop intelligent techniques to automatically detect harmful content, which would allow the large-scale social media monitoring and early detection of undesired situations. Considering the challenges posed by the characteristics of social media content and the cyberaggression task, this paper focuses on the detection of aggressive content in the context of multiple social media sites by exploring diverse types of features. Experimental evaluation conducted on two real-world social media dataset showed the difficulty of the task, confirming the limitations of traditionally used features.Sociedad Argentina de Informática e Investigación Operativ

Artificial Intelligence and Online Extremism: Challenges and Opportunities

Radicalisation is a process that historically used to be triggered mainly through social interactions in places of worship, religious schools, prisons, meeting venues, etc. Today, this process is often initiated on the Internet, where radicalisation content is easily shared, and potential candidates are reached more easily, rapidly, and at an unprecedented scale (Edwards and Gribbon, 2013; Von Behr et al., 2013). In recent years, some terrorist organisations succeeded in leveraging the power of social media to recruit individuals to their cause and ideology (Farwell, 2014). It is often the case that such recruitment attempts are initiated on open social media platforms (e.g., Twitter, Facebook, Tumblr, YouTube) but then move onto private messages and/or encrypted platforms (e.g., WhatsApp, Telegram). Such encrypted communication channels have also been used by terrorist cells and networks to plan their operations (Gartenstein-Ross and Barr). To counteract the activities of such organisations, and to halt the spread of radicalisation content, some governments, social media platforms, and counter-extremism agencies are investing in the creation of advanced information technologies to identify and counter extremism through the development of Artificial Intelligent (AI) solutions (Correa and Sureka, 2013; Agarwal and Sureka 2015a; Scrivens and Davies, 2018). These solutions have three main objectives: (i) understanding the phenomena behind online extremism (the communication flow, the use of propaganda, the different stages of the radicalisation process, the variety of radicalisation channels, etc.), (ii) automatically detecting radical users and content, and (iii) predicting the adoption and spreading of extremist ideas. Despite current advancements in the area, multiple challenges still exist, including: (i) the lack of a common definition of prohibited radical and extremist internet activity, (ii) the lack of solid verification of the datasets collected to develop detection and prediction models, (iii) the lack of cooperation across research fields, since most of the developed technological solutions are neither based on, nor do they take advantage of, existing social theories and studies of radicalisation, (iv) the constant evolution of behaviours associated with online extremism in order to avoid being detected by the developed algorithms (changes in terminology, creation of new accounts, etc.) and, (v) the development of ethical guidelines and legislation to regulate the design and development of AI technology to counter radicalisation. In this book chapter we provide an overview of the current technological advancements towards addressing the problem of online extremism (with a particular focus on Jihadism). We identify some of the limitations of current technologies, and highlight some of the potential opportunities. Our aim is to reflect on the current state of the art and to stimulate discussions on the future design and development of AI technology to target the problem of online extremism

An experimental study on feature engineering and learning approaches for aggression detection in social media

With the widespread of modern technologies and social media networks, a new form of bullying occurring anytime and anywhere has emerged. This new phenomenon, known as cyberaggression or cyberbullying, refers to aggressive and intentional acts aiming at repeatedly causing harm to other person involving rude, insulting, offensive, teasing or demoralising comments through online social media. As these aggressions represent a threatening experience to Internet users, especially kids and teens who are still shaping their identities, social relations and well-being, it is crucial to understand how cyberbullying occurs to prevent it from escalating. Considering the massive information on the Web, the developing of intelligent techniques for automatically detecting harmful content is gaining importance, allowing the monitoring of large-scale social media and the early detection of unwanted and aggressive situations. Even though several approaches have been developed over the last few years based both on traditional and deep learning techniques, several concerns arise over the duplication of research and the difficulty of comparing results. Moreover, there is no agreement regarding neither which type of technique is better suited for the task, nor the type of features in which learning should be based. The goal of this work is to shed some light on the effects of learning paradigms and feature engineering approaches for detecting aggressions in social media texts. In this context, this work provides an evaluation of diverse traditional and deep learning techniques based on diverse sets of features, across multiple social media sites.

Graph based management of temporal data

In recent decades, there has been a significant increase in the use of smart devices and sensors that led to high-volume temporal data generation. Temporal modeling and querying of this huge data have been essential for effective querying and retrieval. However, custom temporal models have the problem of generalizability, whereas the extended temporal models require users to adapt to new querying languages. In this thesis, we propose a method to improve the modeling and retrieval of temporal data using an existing graph database system (i.e., Neo4j) without extending with additional operators. Our work focuses on temporal data represented as intervals (event with a start and end time). We propose a novel way of storing temporal interval as cartesian points where the start time and the end time are stored as the x and y axis of the cartesian coordinate. We present how queries based on Allen’s interval relationships can be represented using our model on a cartesian coordinate system by visualizing these queries. Temporal queries based on Allen’s temporal intervals are then used to validate our model and compare with the traditional way of storing temporal intervals (i.e., as attributes of nodes). Our experimental results on a soccer graph database with around 4000 games show that the spatial representation of temporal interval can provide significant performance (up to 3.5 times speedup) gains compared to a traditional model

AI and extremism in social networks

Studien utforsker hvordan midler som kunstig intelligens, AI- drevne chatbots, kan være kilder man kan regne med som moralske aktører på digitale plattformer og som kan være identifiserbare opprørsmodeller til bekjempelse av ekstremistiske og voldsforherligende ytringer på sosiale medieplattformer. Fremveksten av digital nettverkskommunikasjon har lettet prosessen med sosiale bevegelser, noe fenomenet «Den arabiske våren» tydelig demonstrerer. Sosiale medier har vært et verdifullt verktøy når det gjelder å utvikle kollektive identiteter med en felles ideologi for å fremme et bestemt mål eller en sak og gi alternative plattformer for undertrykte samfunn. Imidlertid forblir virkningen og konsekvensene av sosiale medier i samfunn der maktbalansen forrykkes gjennom fundamentale endringer et bekymringsfullt fenomen. Radikaliserte individer og grupper har også hevdet sin tilstedeværelse på sosiale medieplattformer gjennom å fremme fordommer, hat og vold. Ekstremistiske grupper bruker ulike taktikker for å utøve makten sin på disse plattformene. Bekjempelsen av voldelig ekstremisme på sosiale medieplattformer blir som regel ikke koordinert av aktuelle aktører som regjeringer, sosiale medieselskaper, FN eller andre private organisasjoner. I tillegg har fremdeles ikke forsøk på å konstituere AI til bekjempelse av voldelig ekstremisme blitt gjennomført, men lovende resultater har blitt oppnådd gjennom noen initiativer. Prosjektet som en ‘case study’ ser på den nylige reformen i Etiopia som ble gjennomført av Nobels fredsprisvinner 2019 Abiy Ahmed etter at han tiltrådte som statsminister i Etiopia i april 2018. Etter flere tiår med undertrykkelse har den nye maktovertakelsen der det politiske rommet ble åpnet opp og ytringsfrihet ble tillatt, uventet ført til et skred av etniske gruppers polarisering. Nye etno-ekstremister har dukket frem fra alle kriker og kroker av landet og også fra sin tilværelse i diaspora. Studien ser videre på hvilken rolle sosiale medier til tider spiller ved direkte å presse på for å påvirke til og dermed forårsake voldelige handlinger på grasrota.Ved å bruke en kvalitativ forskningsmetode for ustrukturerte intervjuer med etiopiske brukere av sosiale medier, journalister og aktivister, identifiserer studien kjerneaspektene ved konfliktene og foreslår initiativer som kan brukes til å motvirke voldelig etnisk ekstremisme. Ved å bruke relevant litteratur ser prosjektet videre på innarbeidelsen av kunstig intelligens (AI) i «moralske handlinger» på sosiale medier og hvordan den kan utformes slik at den av seg selv kan ta i bruk moralske beslutningsevner i nettverket. I tillegg ser studien på mulighetene videre for bekjempelse av voldelig ekstremisme og skisserer den spesifikke rollen ikke menneskelige aktører som profesjonelle troll og bots på sosiale medier bør spille for å slåss mot radikalisering som kan føre til voldelige handlinger.Mastergradsoppgave i digital kulturMAHF-DIKULDIKULT35