Analyzing the Targets of Hate in Online Social Media

Social media systems allow Internet users a congenial platform to freely express their thoughts and opinions. Although this property represents incredible and unique communication opportunities, it also brings along important challenges. Online hate speech is an archetypal example of such challenges. Despite its magnitude and scale, there is a significant gap in understanding the nature of hate speech on social media. In this paper, we provide the first of a kind systematic large scale measurement study of the main targets of hate speech in online social media. To do that, we gather traces from two social media systems: Whisper and Twitter. We then develop and validate a methodology to identify hate speech on both these systems. Our results identify online hate speech forms and offer a broader understanding of the phenomenon, providing directions for prevention and detection approaches.Comment: Short paper, 4 pages, 4 table

Reverse Engineering Socialbot Infiltration Strategies in Twitter

Data extracted from social networks like Twitter are increasingly being used to build applications and services that mine and summarize public reactions to events, such as traffic monitoring platforms, identification of epidemic outbreaks, and public perception about people and brands. However, such services are vulnerable to attacks from socialbots $-$ automated accounts that mimic real users $-$ seeking to tamper statistics by posting messages generated automatically and interacting with legitimate users. Potentially, if created in large scale, socialbots could be used to bias or even invalidate many existing services, by infiltrating the social networks and acquiring trust of other users with time. This study aims at understanding infiltration strategies of socialbots in the Twitter microblogging platform. To this end, we create 120 socialbot accounts with different characteristics and strategies (e.g., gender specified in the profile, how active they are, the method used to generate their tweets, and the group of users they interact with), and investigate the extent to which these bots are able to infiltrate the Twitter social network. Our results show that even socialbots employing simple automated mechanisms are able to successfully infiltrate the network. Additionally, using a $2^k$ factorial design, we quantify infiltration effectiveness of different bot strategies. Our analysis unveils findings that are key for the design of detection and counter measurements approaches

YouNICon: YouTube's CommuNIty of Conspiracy Videos

Conspiracy theories are widely propagated on social media. Among various social media services, YouTube is one of the most influential sources of news and entertainment. This paper seeks to develop a dataset, YOUNICON, to enable researchers to perform conspiracy theory detection as well as classification of videos with conspiracy theories into different topics. YOUNICON is a dataset with a large collection of videos from suspicious channels that were identified to contain conspiracy theories in a previous study (Ledwich and Zaitsev 2020). Overall, YOUNICON will enable researchers to study trends in conspiracy theories and understand how individuals can interact with the conspiracy theory producing community or channel. Our data is available at: https://doi.org/10.5281/zenodo.7466262

BeShort: Uma nova abordagem para encurtamento de URLs

Microblogs como o Twitter são sistemas sociais voltados unicamente para a postagem de mensagens com no máximo 140 caracteres. Com o grande uso de mensagens curtas na Web, o uso de encurtadores de URLs está se tornando cada vez mais comum. Sistemas encurtadores traduzem uma URL em uma nova URL, tipicamente com poucos caracteres, e redirecionam requisições à URL encurtada para a URL longa original. Apesar de extremamente eficiente, esses serviços podem introduzir atrasos para seus usuários e têm sido amplamente utilizados para esconder spam, phishing e malware. Esse trabalho apresenta o BeShort, um algoritmo para encurtamento de URLs capaz de evitar tais problemas. Nossa abordagem consiste em substituir partes frequentes de URLs (ex. ``www'' e ``.com.br'') por caracteres UTF-8, normalmente não utilizados em URLs. Para testar nossa abordagem, utilizamos uma base contendo 50 milhões de URLs de dois serviços encurtadores de URL bastante populares. Nossos resultados mostram que o BeShort consegue taxas de encurtamento tão eficientes quanto as taxas praticadas pelos sistemas mais populares atuais