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

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%

Beyond the hashtag : circumventing content moderation on social media

Social media companies make important decisions about what counts as “problematic” content and how they will remove it. Some choose to moderate hashtags, blocking the results for certain tag searches and issuing public service announcements (PSAs) when users search for troubling terms. The hashtag has thus become an indicator of where problematic content can be found, but this has produced limited understandings of how such content actually circulates. Using pro-eating disorder (pro-ED) communities as a case study, this article explores the practices of circumventing hashtag moderation in online pro-ED communities. It shows how (1) untagged pro-ED content can be found without using the hashtag as a search mechanism; (2) users are evading hashtag and other forms of platform policing, devising signals to identify themselves as “pro-ED”; and (3) platforms’ recommendation systems recirculate pro-ED content, revealing the limitations of hashtag logics in social media content moderation

The Future of Freedom of Expression Online

Should social media companies ban Holocaust denial from their platforms? What about conspiracy theorists that spew hate? Does good corporate citizenship mean platforms should remove offensive speech or tolerate it? The content moderation rules that companies develop to govern speech on their platforms will have significant implications for the future of freedom of expression. Given that the prospects for compelling platforms to respect users’ free speech rights are bleak within the U.S. system, what can be done to protect this important right? In June 2018, the United Nations’ top expert for freedom of expression called on companies to align their speech codes with standards embodied in international human rights law, particularly the International Covenant on Civil and Political Rights (ICCPR). After the controversy over de-platforming Alex Jones in August 2018, Twitter’s CEO agreed that his company should root its values in international human rights law and Facebook referenced this body of law in discussing its content moderation policies. This is the first article to explore what companies would need to do to align the substantive restrictions in their speech codes with Article 19 of the ICCPR, which is the key international standard for protecting freedom of expression. In order to examine this issue in a concrete way, this Article assesses whether Twitter’s hate speech rules would need to be modified. This Article also evaluates potential benefits of and concerns with aligning corporate speech codes with this international standard. This Article concludes it would be both feasible and desirable for companies to ground their speech codes in this standard; however, further multi-stakeholder discussions would be helpful to clarify certain issues that arise in translating international human rights law into a corporate context

Bullcoming v. New Mexico: Revisiting Analyst Testimony After Melendez-Diaz

Wallyfy is planned to be a social webmagazine with a focus on good content, with a quality check and moderation of the published content. The author of the study provides Wallyfy with thoughts and ideas about the website content and functionality. The main problem of the study was to explore what makes good content in social media for the potential users of Wallyfy, also using this insight to provide Wallyfy with directions for making decisions regarding both functionality and content.   The theory of the study is being used as a starting point for understanding the phenomenom social media and to easier grasp the problem. The method of the study is based on user- centered thinking in design, where the author seeks to understand the participant’s emotions, values and dreams. Design probes (tasks for the participants) have been used to aid the first steps of the quality data collection, enabling the 5 participants of the study to be a part of the idea generation and to familiarize them with social media. The participants then attended a workshop based on the quality data from the design probes. Further quality data were then derived from the discussion and the creative participation in the workshop.   The relevant data parts were then compiled and orginanised to be presented as the data collection result in the study. The main theme from the data was that the participants valued content more if a personal connection between the user and the content could be made. From a discussion of the data and theory, recommendations and requirements regarding content and functionally for Wallyfy was produced.2014-06-03 14:15, John von Neumann</p

Effectiveness of dismantling strategies on moderated vs. unmoderated online social platforms

Online social networks are the perfect test bed to better understand large-scale human behavior in interacting contexts. Although they are broadly used and studied, little is known about how their terms of service and posting rules affect the way users interact and information spreads. Acknowledging the relation between network connectivity and functionality, we compare the robustness of two different online social platforms, Twitter and Gab, with respect to dismantling strategies based on the recursive censor of users characterized by social prominence (degree) or intensity of inflammatory content (sentiment). We find that the moderated (Twitter) vs unmoderated (Gab) character of the network is not a discriminating factor for intervention effectiveness. We find, however, that more complex strategies based upon the combination of topological and content features may be effective for network dismantling. Our results provide useful indications to design better strategies for countervailing the production and dissemination of anti-social content in online social platforms