Impact Of Content Features For Automatic Online Abuse Detection

Online communities have gained considerable importance in recent years due to the increasing number of people connected to the Internet. Moderating user content in online communities is mainly performed manually, and reducing the workload through automatic methods is of great financial interest for community maintainers. Often, the industry uses basic approaches such as bad words filtering and regular expression matching to assist the moderators. In this article, we consider the task of automatically determining if a message is abusive. This task is complex since messages are written in a non-standardized way, including spelling errors, abbreviations, community-specific codes... First, we evaluate the system that we propose using standard features of online messages. Then, we evaluate the impact of the addition of pre-processing strategies, as well as original specific features developed for the community of an online in-browser strategy game. We finally propose to analyze the usefulness of this wide range of features using feature selection. This work can lead to two possible applications: 1) automatically flag potentially abusive messages to draw the moderator's attention on a narrow subset of messages ; and 2) fully automate the moderation process by deciding whether a message is abusive without any human intervention

Abusive Language Detection in Online Conversations by Combining Content-and Graph-based Features

In recent years, online social networks have allowed worldwide users to meet and discuss. As guarantors of these communities, the administrators of these platforms must prevent users from adopting inappropriate behaviors. This verification task, mainly done by humans, is more and more difficult due to the ever growing amount of messages to check. Methods have been proposed to automatize this moderation process, mainly by providing approaches based on the textual content of the exchanged messages. Recent work has also shown that characteristics derived from the structure of conversations, in the form of conversational graphs, can help detecting these abusive messages. In this paper, we propose to take advantage of both sources of information by proposing fusion methods integrating content-and graph-based features. Our experiments on raw chat logs show that the content of the messages, but also of their dynamics within a conversation contain partially complementary information, allowing performance improvements on an abusive message classification task with a final F-measure of 93.26%

The Bullying Game: Sexism Based Toxic Language Analysis on Online Games Chat Logs by Text Mining

As a unique type of social network, the online gaming industry is a fast-growing, changing, and men-dominated field which attracts diverse backgrounds. Being dominated by male users, game developers, game players, game investors, the non-inclusiveness and gender inequality reside as salient problems in the community. In the online gaming communities, most women players report toxic and offensive language or experiences of verbal abuse. Symbolic interactionists and feminists assume that words matter since the use of particular language and terms can dehumanize and harm particular groups such as women. Identifying and reporting the toxic behavior, sexism, and harassment that occur in online games is a critical need in preventing cyberbullying, and it will help gender diversity and equality grow in the online gaming industry. However, the research on this topic is still rare, except for some milestone studies. This paper aims to contribute to the theory and practice of sexist toxic language detection in the online gaming community, through the automatic detection and analysis of toxic comments in online games chat logs. We adopted the MaXQDA tool as a data visualization technique to reveal the most frequently used toxic words used against women in online gaming communities. We also applied the Naïve Bayes Classifier for text mining to classify if a chat log content is sexist and toxic. We also refined the text mining model Laplace estimator and re-tested the model’s accuracy. The study also revealed that the accuracy of the Naïve Bayes Classifier did not change by the Laplace estimator. The findings of the study are expected to raise awareness about the use of gender-based toxic language in the online gaming community. Moreover, the proposed mining model can inspire similar research on practical tools to help moderate the use of sexist toxic language and disinfect these communities from gender-based discrimination and sexist bullying

Graph-based Features for Automatic Online Abuse Detection

While online communities have become increasingly important over the years, the moderation of user-generated content is still performed mostly manually. Automating this task is an important step in reducing the financial cost associated with moderation, but the majority of automated approaches strictly based on message content are highly vulnerable to intentional obfuscation. In this paper, we discuss methods for extracting conversational networks based on raw multi-participant chat logs, and we study the contribution of graph features to a classification system that aims to determine if a given message is abusive. The conversational graph-based system yields unexpectedly high performance , with results comparable to those previously obtained with a content-based approach

The impact of avatar attractiveness and customization on online gamers’ flow and loyalty

This study aims to examine the factor the avatar attractiveness and customization influencing online gamer flow and loyalty. The data were collected from 501 students from Karachi, mainly the students of Iqra University through online questionnaire for analyzing the research. The model PLS-SEM partial least square was used. We use regression analysis to analyze the association between variables. This study finds that avatar customization fuels avatar identification, which is to turn creates flow and loyalty in online game. This study also apply social identity and flow theories to explain how avatar attractiveness and customization contribute to online gamers flow and loyalty. The limitation of the study is we have conducted this research in Iqra University, second limitation is we have conducted research on 500 people. As it was online survey so most of the people take it for granted so result could be fluctuated. Furthermore, researchers should extend this study and can add more market generated content according to respondent’s desires into research model to engage more and more online gamers

Investigating the relationship between playing violent video games and viewing violent TV programmes and aggressive behaviour among pre-teens

This study aimed to investigate the relationship between the playing of violent video games and the viewing of violent TV programmes and aggressive behaviour among pre-teens. According to McGahee, Kemp and Tingen (2000), pre-teens referred to preadolescent children who are usually between 9 and 12 years of age. A total of 450 pre-teens aged 11 were randomly selected from nine schools in the state of Selangor, Malaysia, to participate in this study. This study employed a correlation research design and the data were analysed using both descriptive and inferential statistics to address the research objectives. The data were analysed to identify the top 10 favourite video games played by pre-teens in this study. Eight out of 10 video games played by pre-teens were found to be violent in nature. In addition, the top 10 favourite violent TV programmes viewed by pre-teens in this study were also identified. Findings from this study showed that there was a significant difference in the mean score of playing violent video games [t(257)=6.979, p<0.01] and viewing violent TV programmes [t(440)=3.544, p<0.01) between boys and girls who participated in the study. Moreover, the results from this study revealed there was a significant and positive relationship between playing violent video games (r=0.167, p<0.01), viewing violent TV programmes (r=0.126, p=0.000) and aggressive behaviour demonstrated by pre-teens. Multiple regression analysis showed that 39.4% of the variances in pre-teen physical aggression could be explained by both the playing of violent video games and the viewing of violent TV programmes, with the playing of violent video games as a stronger predictor of physical aggressive behaviour in pre-teens (ß=0.238, p=0.025)

Challenges in Modifying Existing Scales for Detecting Harassment in Individual Tweets

In an effort to create new sociotechnical tools to combat online harassment, we developed a scale to detect and measure verbal violence within individual tweets. Unfortunately, we found that the scale, based on scales effective at detecting harassment offline, was unreliable for tweets. Here, we begin with information about the development and validation of our scale, then discuss the scale’s shortcomings for detecting harassment in tweets, and explore what we can learn from this scale’s failures. We explore how rarity, context, and individual coder’s differences create challenges for detecting verbal violence in individual tweets. We also examine differences in on- and offline harassment that limit the utility of existing harassment measures for online contexts. We close with a discussion of potential avenues for future work in automated harassment detection

Machine learning and semantic analysis of in-game chat for cyber bullying

One major problem with cyberbullying research is the lack of data, since researchers are traditionally forced to rely on survey data where victims and perpetrators self-report their impressions. In this paper, an automatic data collection system is presented that continuously collects in-game chat data from one of the most popular online multi-player games: World of Tanks. The data was collected and combined with other information about the players from available online data services. It presents a scoring scheme to enable identification of cyberbullying based on current research. Classification of the collected data was carried out using simple feature detection with SQL database queries and compared to classification from AI-based sentiment text analysis services that have recently become available and further against manually classified data using a custom-built classificationclient built for this paper. The simple SQL classification proved to be quite useful at identifying some features of toxic chat such as the use of bad language or racist sentiments, however the classification by the more sophisticated online sentiment analysis services proved to be disappointing. The results were then examined for insights into cyberbullying within this game and it was shown that it should be possible to reduce cyberbullying within the World of Tanks game by a significant factor by simply freezing the player’s ability to communicate through the in-game chat function for a short period after the player is killed within a match. It was also shown that very new players are much less likely to engage in cyberbullying, suggesting that it may be a learned behaviour from other players