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%

Text Mining and Cybercrime

This chapter describes the state of technology for studying Internet crimes against children, specifically sexual predation and cyberbullying. We begin by presenting a survey of relevant research articles that are related to the study of cybercrime. This survey includes a discussion of our work on the classification of chat logs that contain bullying or predatory behavior. Many commercial enterprises have developed parental control software to monitor these behaviors, and the latest version of some of these tools provides features that profess to protect children against predators and bullies. The chapter concludes with a discussion of these products and offers suggestions for continued research in this interesting and timely sub-field of text mining. 1.

Innovative Self-Confidence Webinar Intervention for Depression in the Workplace: A Focus Group Study and Systematic Development

Brief face-to-face self-confidence workshops were effective in reducing depression among the public. Technological advances have enabled traditional face-to-face interventions to be adapted using unique technology-mediated platforms. This article details the formative development of a self-confidence web-based seminar (webinar) intervention for workplace depression. The first section discusses a qualitative study that explores the feasibility and acceptability of adapting the self-confidence workshops into a webinar platform on employees in the workplace. The second section describes the systematic development of this new webinar intervention informed by the qualitative study findings, a published systematic review, and previous face-to-face self-confidence workshops. The qualitative study involves three focus groups (n = 10) conducted in a small organization. Three themes were identified relevant to the running of the new self-confidence webinars in the workplace: personal (content, time and duration preference, features of the webinar, individual participation, personalization), interpersonal (stigma from others, engagement with participants/presenter, moderated interaction), and organizational (endorsement from management, work demand). For the intervention development, the format, structure, features, and content of the self-confidence webinar intervention are described. Features such as file sharing, virtual whiteboard, live chat, and poll are explained with the intervention primarily based on cognitive behavior therapy and coping flexibility concepts.</p

A tool for internet chatroom surveillance

Abstract. Internet chatrooms are common means of interaction and communications, and they carry valuable information about formal or ad-hoc formation of groups with diverse objectives. This work presents a fully automated surveillance system for data collection and analysis in Internet chatrooms. The system has two components: First, it has an eavesdropping tool which collects statistics on individual (chatter) and chatroom behavior. This data can be used to profile a chatroom and its chatters. Second, it has a computational discovery algorithm based on Singular Value Decomposition (SVD) to locate hidden communities and communication patterns within a chatroom. The eavesdropping tool is used for fine tuning the SVD-based discovery algorithm which can be deployed in real-time and requires no semantic information processing. The evaluation of the system on real data shows that (i) statistical properties of different chatrooms vary significantly, thus profiling is possible, (ii) SVD-based algorithm has up to 70-80 % accuracy to discover groups of chatters.