Detecting Offensive Statements towards Foreigners in Social Media

Recently, politicians and media companies identified an increasing number of offensive statements directed against foreigners and refugees in Europe. In Germany, for example, the political group “Pegida” drew international attention by frequently publishing offensive content concerning the religion of Islam. As a consequence, the German government and the social network Facebook cooperate to address this problem by creating a task force to manually detect offensive statements towards refugees and foreigners. In this work, we propose an approach to automatically detect such statements aiding personnel in this labor-intensive task. In contrast to existing work, we assess severity values to offensive statements and identify the referenced targets. This way, we are able to selectively detect hostility towards foreigners. To evaluate our approach, we develop a dataset containing offensive statements including their target. As a result, a substantial amount of offensive statements and a moderate amount of the referenced victims was detected correctly

A Proposed Architecture for Continuous Web Monitoring Through Online Crawling of Blogs

Getting informed of what is registered in the Web space on time, can greatly help the psychologists, marketers and political analysts to familiarize, analyse, make decision and act correctly based on the society`s different needs. The great volume of information in the Web space hinders us to continuously online investigate the whole space of the Web. Focusing on the considered blogs limits our working domain and makes the online crawling in the Web space possible. In this article, an architecture is offered which continuously online crawls the related blogs, using focused crawler, and investigates and analyses the obtained data. The online fetching is done based on the latest announcements of the ping server machines. A weighted graph is formed based on targeting the important key phrases, so that a focused crawler can do the fetching of the complete texts of the related Web pages, based on the weighted graph.Comment: 10 pages, 2 figure

Cognitive Dissonance pada Konteks Berkomunikasi dan Mencari Informasi di Ruang Digital: Fenomena Selective Exposure

In the digital space that is now increasingly dominating our lives, there are challenges and new social situations that require us to adapt. Cognitive Dissonance Theory (CDT) places a focus on seeing human behavior in situations that are incompatible with the cognition he has. After six decades of CDT's existence, there have been many derivative concepts that can be used to analyze causation in this dissonance situation. Selective exposure is a concept derived from CDT that has received attention from researchers in analyzing the phenomenon of filter bubbles and echo chambers. To examine the application of CDT in a contemporary context, in this digital space, this study was conducted by making a literature review that focuses on elaborating on the theory and context of CDT in use. Using qualitative content analysis from a collection of previous studies, this study maps the relevance of theory to the context of situations in the digital space. The conclusion is that CDT, both based on basic assumptions and derived concepts tested by other researchers, can objectively predict the cause-and-effect of a situation that triggers dissonance in a person's cognition when a situation with similar conditions occurs

Cognitive Dissonance pada Konteks Berkomunikasi dan Mencari Informasi di Ruang Digital: Fenomena Selective Exposure

In the digital space that is now increasingly dominating our lives, there are challenges and new social situations that require us to adapt. Cognitive Dissonance Theory (CDT) places a focus on seeing human behavior in situations that are incompatible with the cognition he has. After six decades of CDT's existence, there have been many derivative concepts that can be used to analyze causation in this dissonance situation. Selective exposure is a concept derived from CDT that has received attention from researchers in analyzing the phenomenon of filter bubbles and echo chambers. To examine the application of CDT in a contemporary context, in this digital space, this study was conducted by making a literature review that focuses on elaborating on the theory and context of CDT in use. Using qualitative content analysis from a collection of previous studies, this study maps the relevance of theory to the context of situations in the digital space. The conclusion is that CDT, both based on basic assumptions and derived concepts tested by other researchers, can objectively predict the cause-and-effect of a situation that triggers dissonance in a person's cognition when a situation with similar conditions occurs

Controversy Analysis and Detection

Seeking information on a controversial topic is often a complex task. Alerting users about controversial search results can encourage critical literacy, promote healthy civic discourse and counteract the filter bubble effect, and therefore would be a useful feature in a search engine or browser extension. Additionally, presenting information to the user about the different stances or sides of the debate can help her navigate the landscape of search results beyond a simple list of 10 links . This thesis has made strides in the emerging niche of controversy detection and analysis. The body of work in this thesis revolves around two themes: computational models of controversy, and controversies occurring in neighborhoods of topics. Our broad contributions are: (1) Presenting a theoretical framework for modeling controversy as contention among populations; (2) Constructing the first automated approach to detecting controversy on the web, using a KNN classifier that maps from the web to similar Wikipedia articles; and (3) Proposing a novel controversy detection in Wikipedia by employing a stacked model using a combination of link structure and similarity. We conclude this work by discussing the challenging technical, societal and ethical implications of this emerging research area and proposing avenues for future work

Sentiment analysis and real-time microblog search

This thesis sets out to examine the role played by sentiment in real-time microblog search. The recent prominence of the real-time web is proving both challenging and disruptive for a number of areas of research, notably information retrieval and web data mining. User-generated content on the real-time web is perhaps best epitomised by content on microblogging platforms, such as Twitter. Given the substantial quantity of microblog posts that may be relevant to a user query at a given point in time, automated methods are required to enable users to sift through this information. As an area of research reaching maturity, sentiment analysis offers a promising direction for modelling the text content in microblog streams. In this thesis we review the real-time web as a new area of focus for sentiment analysis, with a specific focus on microblogging. We propose a system and method for evaluating the effect of sentiment on perceived search quality in real-time microblog search scenarios. Initially we provide an evaluation of sentiment analysis using supervised learning for classi- fying the short, informal content in microblog posts. We then evaluate our sentiment-based filtering system for microblog search in a user study with simulated real-time scenarios. Lastly, we conduct real-time user studies for the live broadcast of the popular television programme, the X Factor, and for the Leaders Debate during the Irish General Election. We find that we are able to satisfactorily classify positive, negative and neutral sentiment in microblog posts. We also find a significant role played by sentiment in many microblog search scenarios, observing some detrimental effects in filtering out certain sentiment types. We make a series of observations regarding associations between document-level sentiment and user feedback, including associations with user profile attributes, and users’ prior topic sentiment

Wikum: Bridging Discussion Forums and Wikis Using Recursive Summarization

Large-scale discussions between many participants abound on the internet today, on topics ranging from political arguments to group coordination. But as these discussions grow to tens of thousands of posts, they become ever more difficult for a reader to digest. In this article, we describe a workflow called recursive summarization, implemented in our Wikum prototype, that enables a large population of readers or editors to work in small doses to refine out the main points of the discussion. More than just a single summary, our workflow produces a summary tree that enables a reader to explore distinct subtopics at multiple levels of detail based on their interests. We describe lab evaluations showing that (i) Wikum can be used more effectively than a control to quickly construct a summary tree and (ii) the summary tree is more effective than the original discussion in helping readers identify and explore the main topics