Brute - Force Sentence Pattern Extortion from Harmful Messages for Cyberbullying Detection

Cyberbullying, or humiliating people using the Internet, has existed almost since the beginning ofInternet communication.The relatively recent introduction of smartphones and tablet computers has caused cyberbullying to evolve into a serious social problem. In Japan, members of a parent-teacher association (PTA)attempted to address the problem by scanning the Internet for cyber bullying entries. To help these PTA members and other interested parties confront this difficult task we propose a novel method for automatic detection of malicious Internet content. This method is based on a combinatorial approach resembling brute-force search algorithms, but applied in language classification. The method extracts sophisticated patterns from sentences and uses them in classification. The experiments performed on actual cyberbullying data reveal an advantage of our method vis-à-visprevious methods. Next, we implemented the method into an application forAndroid smartphones to automatically detect possible harmful content in messages. The method performed well in the Android environment, but still needs to be optimized for time efficiency in order to be used in practic

Volunteer Computing Simulation Using Repast And Mason

Volunteer environments usually consist of a large number of computing nodes,with highly dynamic characteristics, therefore reliable models for a planning ofthe whole computing are highly desired. An easy to implement approach to mo-delling and simulation of such environments may employ agent-based universalsimulation frameworks, such as RePast or MASON. In the course of the paperthe above-mentioned simulation frameworks are adapted to support simulationof volunteer computing. After giving implementation details, selected resultsconcerning computing time and speedup are given and are compared with theones obtained from an actual volunteer environment

Looking for Razors and Needles in a Haystack: Multifaceted Analysis of Suicidal Declarations on Social Media—A Pragmalinguistic Approach

In this paper, we study language used by suicidal users on Reddit social media platform. To do that, we firstly collect a large-scale dataset of Reddit posts and annotate it with highly trained and expert annotators under a rigorous annotation scheme. Next, we perform a multifaceted analysis of the dataset, including: (1) the analysis of user activity before and after posting a suicidal message, and (2) a pragmalinguistic study on the vocabulary used by suicidal users. In the second part of the analysis, we apply LIWC, a dictionary-based toolset widely used in psychology and linguistic research, which provides a wide range of linguistic category annotations on text. However, since raw LIWC scores are not sufficiently reliable, or informative, we propose a procedure to decrease the possibility of unreliable and misleading LIWC scores leading to misleading conclusions by analyzing not each category separately, but in pairs with other categories. The analysis of the results supported the validity of the proposed approach by revealing a number of valuable information on the vocabulary used by suicidal users and helped to pin-point false predictors. For example, we were able to specify that death-related words, typically associated with suicidal posts in the majority of the literature, become false predictors, when they co-occur with apostrophes, even in high-risk subreddits. On the other hand, the category-pair based disambiguation helped to specify that death becomes a predictor only when co-occurring with future-focused language, informal language, discrepancy, or 1st person pronouns. The promising applicability of the approach was additionally analyzed for its limitations, where we found out that although LIWC is a useful and easily applicable tool, the lack of any contextual processing makes it unsuitable for application in psychological and linguistic studies. We conclude that disadvantages of LIWC can be easily overcome by creating a number of high-performance AI-based classifiers trained for annotation of similar categories as LIWC, which we plan to pursue in future work