Psychological Analysis on the Issues of Violence Against Women in Language and Media

Language and media are effective entities to perpetuate male domination over women. Both are representations of various social conflicts, interests, power and hegemony. Through Psychological analysis, this study aims to reveal how both of them can establish the process of 'marginalizing' women. This study used a qualitative method with a literature review approach. The results showed that violence against women in language and the media is an invisible form of violence. Language is something that has a purpose (teleology) in itself, which is conditioned by various environmental interests. In patriarchal culture, language is used to build a bad image of women with the aim of strengthening the position of men as the dominant group. The bad image is then transplanted by the media, made into a universe of discourse and implanted into collective consciousness as the public's subconscious imagination. As a result, whether we realize it or not, women are treated in a subordinate way, but also define themselves in a subordinate way according to men's eyes. Bahasa dan media adalah entitas yang efektif untuk mengekalkan dominasi laki-laki atas perempuan. Keduanya merupakan representasi dari pagelaran berbagai konflik sosial, kepentingan, kekuasaan serta hegemoni. Melalui analisis Psikologi, kajian ini bertujuan untuk mengungkapbagaimana keduanya dapat memapankan proses ‘memarjinalkan’ kaum perempuan. Kajian ini menggunakan metode kualitatif deskriptif dengan teknik pengambilan data sekunder melalui kajian literatur. Hasil penelitian menunjukan bahwa kekerasan terhadap perempuan dalam bahasa dan media adalah bentuk kekerasan yang tidak kasat mata. Bahasa merupakan ekspresi seseorang untuk mewakili logika, struktur budaya, sosial, psikologi, filosofi, dan politik yang dianut oleh penuturnya. Ia memiliki ketertujuan (teleologi) di dalam dirinya, yang terkondisi oleh pelbagai interes lingkungannya. Dalam budaya patriarkhi, Bahasa digunakan untuk membagun image buruk pada perempuan dengan tujuan mengukuhkan posisi laki-laki sebagai kelompok dominan. Image buruk tersebut kemudian dicangkok oleh media, dijadikan pemahaman universal, dan ditanamkan ke dalam kesadaran kolektif sebagai imajinasi alam bawah sadar masyarakat. Wal-hasil, disadari atau tidak, perempuan selain diperlakukan secara subordinatif, juga mendefinisikan diri secara subordinatif sesuai dengan perspektif laki-laki

Design of a Controlled Language for Critical Infrastructures Protection

We describe a project for the construction of controlled language for critical infrastructures protection (CIP). This project originates from the need to coordinate and categorize the communications on CIP at the European level. These communications can be physically represented by official documents, reports on incidents, informal communications and plain e-mail. We explore the application of traditional library science tools for the construction of controlled languages in order to achieve our goal. Our starting point is an analogous work done during the sixties in the field of nuclear science known as the Euratom Thesaurus.JRC.G.6-Security technology assessmen

Players Unleashed! Modding The Sims and the Culture of Gaming

Siirretty Doriast

Extracting Insights from Differences: Analyzing Node-aligned Social Graphs

Social media and network research often focus on the agreement between different entities to infer connections, recommend actions and subscriptions and even improve algorithms via ensemble methods. However, studying differences instead of similarities can yield useful insights in all these cases. We can infer and understand inter-community interactions (including ideological and user-based community conflicts, hierarchical community relations) and improve community detection algorithms via insights gained from differences among entities such as communities, users and algorithms. When the entities are communities or user groups, we often study the difference via node-aligned networks, which are networks with the same set of nodes but different sets of edges. The edges define implicit connections which we can infer via similarities or differences between two nodes. We perform a set of studies to identify and understand differences among user groups using Reddit, where the subreddit structure provides us with pre-defined user groups. Studying the difference between author overlap and textual similarity among different subreddits, we find misaligned edges and networks which expose subreddits at ideological 'war', community fragmentation, asymmetry of interactions involving subreddits based on marginalized social groups and more. Differences in perceived user behavior across different subreddits allow us to identify subreddit conflicts and features which can implicate communal misbehavior. We show that these features can be used to identify some subreddits banned by Reddit. Applying the idea of differences in community detection algorithms helps us identify problematic community assignments where we can ask for human help in categorizing a node in a specific community. It also gives us an idea of the overall performance of a particular community detection algorithm on a particular network input. In general, these improve ensemble community detection techniques. We demonstrate this via CommunityDiff (a community detection and visualization tool), which compares and contrasts different algorithms and incorporates user knowledge in community detection output. We believe the idea of gaining insights from differences can be applied to several other problems and help us understand and improve social media interactions and research.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/149801/1/srayand_1.pd

CHORUS Deliverable 2.1: State of the Art on Multimedia Search Engines

Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socio-economic perspective. The technical perspective includes an up to date view on content based indexing and retrieval technologies, multimedia search in the context of mobile devices and peer-to-peer networks, and an overview of current evaluation and benchmark inititiatives to measure the performance of multimedia search engines. From a socio-economic perspective we inventorize the impact and legal consequences of these technical advances and point out future directions of research

Players Unleashed ! Modding The Sims and the Culture of Gaming

The author of this hugely informative study explores the question of what happens when players practise and negotiate computer code, various ideologies, and the game itself by modding (modifying a game) in the context of The Sims, the bestselling computer game of all time.Sihvonen examines the technical and material specificities of The Sims mods, as well as their cultural context. Viewed as a manifestation of participatory culture, modding makes PC games ultimately malleable: players reconfigure the game by creating new content, altering the code and changing the behaviours of the game engine. Using a semiotic framework, Sihvonen suggests a signification process that includes representation, interpretation, investigation and experimentation with the game system and rules. From its historical roots in the shoot’em up games, the author bares the fascinating evolution and dynamics of modding, where gender stereotypes, the thrills of hacking and living the Sims’ American Dream intersect with the aesthetic and operational dimensions of modding

Navigating The Manosphere: An Examination Of The Incel Movements’ Attitudes Of Sexual Aggression And Violence Against Women

There is a considerable and established literature on the topic of violence against women. Yet, there remains understudied areas of foci with lesser attention paid to its occurrence within online and digital contexts. Of particular interest is the networked misogyny and sexism of the online group that self-identifies as “involuntary celibates”, or “incels”. Drawing on data collected from online forums and chat rooms, the language and discourse of this particular group are analyzed through an integrated conceptual framework encompassing hegemonic masculinity, aggrieved entitlement, and patriarchy to better understand the prevalence and types of violence promoted by “incels”. Emerging themes revealed the pervasiveness of rape culture, pro-attitudes of violence against women, male victimization and oppression, sexual entitlement, and masculinity crises

Detecting cyberbullying and cyberaggression in social media

Cyberbullying and cyberaggression are increasingly worrisome phenomena affecting people across all demographics. More than half of young social media users worldwide have been exposed to such prolonged and/or coordinated digital harassment. Victims can experience a wide range of emotions, with negative consequences such as embarrassment, depression, isolation from other community members, which embed the risk to lead to even more critical consequences, such as suicide attempts. In this work, we take the first concrete steps to understand the characteristics of abusive behavior in Twitter, one of today’s largest social media platforms. We analyze 1.2 million users and 2.1 million tweets, comparing users participating in discussions around seemingly normal topics like the NBA, to those more likely to be hate-related, such as the Gamergate controversy, or the gender pay inequality at the BBC station. We also explore specific manifestations of abusive behavior, i.e., cyberbullying and cyberaggression, in one of the hate-related communities (Gamergate). We present a robust methodology to distinguish bullies and aggressors from normal Twitter users by considering text, user, and network-based attributes. Using various state-of-the-art machine-learning algorithms, we classify these accounts with over 90% accuracy and AUC. Finally, we discuss the current status of Twitter user accounts marked as abusive by our methodology and study the performance of potential mechanisms that can be used by Twitter to suspend users in the future