The computer science research community has became increasingly interested in the study of social media due to their pervasiveness in the everyday life of millions of individuals. Methodological questions and technical challenges abound as more and more data from social platforms become available for analysis. This data deluge not only yields the unprecedented opportunity to unravel questions about online individuals' behavior at scale, but also allows to explore the potential perils that the massive adoption of social media brings to our society. These communication channels provide plenty of incentives (both economical and social) and opportunities for abuse. As social media activity became increasingly intertwined with the events in the offline world, individuals and organizations have found ways to exploit these platforms to spread misinformation, to attack and smear others, or to deceive and manipulate. During crises, social media have been effectively used for emergency response, but fear-mongering actions have also triggered mass hysteria and panic. Criminal gangs and terrorist organizations like ISIS adopt social media for propaganda and recruitment. Synthetic activity and social bots have been used to coordinate orchestrated astroturf campaigns, to manipulate political elections and the stock market. The lack of effective content verification systems on many of these platforms, including Twitter and Facebook, rises concerns when younger users become exposed to cyber-bulling, harassment, or hate speech, inducing risks like depression and suicide. This article illustrates some of the recent advances facing these issues and discusses what it remains to be done, including the challenges to address in the future to make social media a more useful and accessible, safer and healthier environment for all users.
Connecting to social media networks becomes a daily task for the majority of people around the world, and the amount of shared information is growing exponentially. Thus, controlling the way in which people communicate is necessary, in order to protect them from disorientation, conflicts, aggressions, etc. In this paper, we conduct a statistical study on the cyber-bullying and the abusive content in social media (i.e. Facebook), where we try to spot the online behaviour of the abusers in the Algerian community. More specifically, we have involved 200 Facebook users from different regions among…
In the last decade drug overdose deaths reached staggering proportions in the US. Besides the raw yearly deaths count that is worrisome per se, an alarming picture comes from the steep acceleration of such rate that increased by 21% from 2015 to 2016. While traditional public health surveillance suffers from its own biases and limitations, digital epidemiology offers a new lens to extract signals from Web and Social Media that might be complementary to official statistics. In this paper we present a computational approach to identify a digital cohort that might provide an updated and complemen…
Illicit drug trade via social media sites, especially photo-oriented Instagram, has become a severe problem in recent years. As a result, tracking drug dealing and abuse on Instagram is of interest to law enforcement agencies and public health agencies. In this paper, we propose a novel approach to detecting drug abuse and dealing automatically by utilizing multimodal data on social media. This approach also enables us to identify drug-related posts and analyze the behavior patterns of drug-related user accounts. To better utilize multimodal data on social media, multimodal analysis methods in…
Indian Institute of Technology, Kharagpur（印度理工学院卡格普尔分校）
Reducing hateful and offensive content in online social media pose a dual problem for the moderators. On the one hand, rigid censorship on social media cannot be imposed. On the other, the free flow of such content cannot be allowed. Hence, we require efficient abusive language detection system to detect such harmful content in social media. In this paper, we present our machine learning model, HateMonitor, developed for Hate Speech and Offensive Content Identification in Indo-European Languages (HASOC), a shared task at FIRE 2019. We have used a Gradient Boosting model, along with BERT and LA…
Online social media (OSM) has a enormous influence in today's world. Some individuals view OSM as fertile ground for abuse and use it to disseminate misinformation and political propaganda, slander competitors, and spread spam. The crowdturfing industry employs large numbers of bots and human workers to manipulate OSM and misrepresent public opinion. The detection of online discussion topics manipulated by OSM \emph{abusers} is an emerging issue attracting significant attention. In this paper, we propose an approach for quantifying the authenticity of online discussions based on the similarity…
Domestic Violence against women is now recognized to be a serious and widespread problem worldwide. Domestic Violence and Abuse is at the root of so many issues in society and considered as the societal tabooed topic. Fortunately, with the popularity of social media, social welfare communities and victim support groups facilitate the victims to share their abusive stories and allow others to give advice and help victims. Hence, in order to offer the immediate resources for those needs, the specific messages from the victims need to be alarmed from other messages. In this paper, we regard inten…
Element AI（元素人工智能公司）；Amnesty International（国际特赦组织）
We report the first, to the best of our knowledge, hand-in-hand collaboration between human rights activists and machine learners, leveraging crowd-sourcing to study online abuse against women on Twitter. On a technical front, we carefully curate an unbiased yet low-variance dataset of labeled tweets, analyze it to account for the variability of abuse perception, and establish baselines, preparing it for release to community research efforts. On a social impact front, this study provides the technical backbone for a media campaign aimed at raising public and deciders' awareness and elevating t…
The use of short text messages in social media and instant messaging has become a popular communication channel during the last years. This rising popularity has caused an increment in messaging threats such as spam, phishing or malware as well as other threats. The processing of these short text message threats could pose additional challenges such as the presence of lexical variants, SMS-like contractions or advanced obfuscations which can degrade the performance of traditional filtering solutions. By using a real-world SMS data set from a large telecommunications operator from the US and a …
While social media offers freedom of self-expression, abusive language carry significant negative social impact. Driven by the importance of the issue, research in the automated detection of abusive language has witnessed growth and improvement. However, these detection models display a reliance on strongly indicative keywords, such as slurs and profanity. This means that they can falsely (1a) miss abuse without such keywords or (1b) flag non-abuse with such keywords, and that (2) they perform poorly on unseen data. Despite the recognition of these problems, gaps and inconsistencies remain in …
Institute of Engineering and Technology（工程技术学院）；Indian Institute of Technology（印度理工学院）；Nanyang Technological University（南洋理工大学）
In the past few years, social media has risen as a platform where people express and share personal incidences about abuse, violence and mental health issues. There is a need to pinpoint such posts and learn the kind of response expected. For this purpose, we understand the sentiment that a personal story elicits on different posts present on different social media sites, on the topics of abuse or mental health. In this paper, we propose a method supported by hand-crafted features to judge if the post requires an empathetic response. The model is trained upon posts from various web-pages and c…
Aristotle University of Thessaloniki（塞萨洛尼基亚里士多德大学）；Telefonica Research（西班牙电信研究院）；University of Alabama（阿拉巴马大学）
Hate speech, offensive language, sexism, racism and other types of abusive behavior have become a common phenomenon in many online social media platforms. In recent years, such diverse abusive behaviors have been manifesting with increased frequency and levels of intensity. This is due to the openness and willingness of popular media platforms, such as Twitter and Facebook, to host content of sensitive or controversial topics. However, these platforms have not adequately addressed the problem of online abusive behavior, and their responsiveness to the effective detection and blocking of such i…
The year 2020 will be remembered for two events of global significance: the COVID-19 pandemic and 2020 U.S. Presidential Election. In this chapter, we summarize recent studies using large public Twitter data sets on these issues. We have three primary objectives. First, we delineate epistemological and practical considerations when combining the traditions of computational research and social science research. A sensible balance should be struck when the stakes are high between advancing social theory and concrete, timely reporting of ongoing events. We additionally comment on the computationa…
Social media platforms attempting to curb abuse and misinformation have been accused of political bias. We deploy neutral social bots who start following different news sources on Twitter, and track them to probe distinct biases emerging from platform mechanisms versus user interactions. We find no strong or consistent evidence of political bias in the news feed. Despite this, the news and information to which U.S. Twitter users are exposed depend strongly on the political leaning of their early connections. The interactions of conservative accounts are skewed toward the right, whereas liberal…
Several projects have shown the feasibility to use textual social media data to track public health concerns, such as temporal influenza patterns or geographical obesity patterns. In this paper, we look at whether geo-tagged images from Instagram also provide a viable data source. Especially for "lifestyle" diseases, such as obesity, drinking or smoking, images of social gatherings could provide information that is not necessarily shared in, say, tweets. In this study, we explore whether (i) tags provided by the users and (ii) annotations obtained via automatic image tagging are indeed valuabl…
The Uli Dataset: An Exercise in Experience Led Annotation of oGBV
University of Copenhagen（哥本哈根大学）；Tattle Civic Tech（塔特尔公民科技）；Bebaak Collective（贝巴克集体）；National Council of Women Leaders（全国女性领袖委员会）；Chambal Media/Khabar Lahariya（昌巴尔媒体/赫拉哈里亚新闻）；Center for Internet and Society（互联网与社会中心）
Malicious actors exploit social media to inflate stock prices, sway elections, spread misinformation, and sow discord. To these ends, they employ tactics that include the use of inauthentic accounts and campaigns. Methods to detect these abuses currently rely on features specifically designed to target suspicious behaviors. However, the effectiveness of these methods decays as malicious behaviors evolve. To address this challenge, we propose a general language for modeling social media account behavior. Words in this language, called BLOC, consist of symbols drawn from distinct alphabets repre…
Social media platforms are revolutionizing the way users communicate by increasing the exposure to highly stigmatized issues in the society. Sexual abuse is one such issue that recently took over social media via attaching the hashtag #metoo to the shared posts. Individuals with different backgrounds and ethnicities began sharing their unfortunate personal experiences of being assaulted. Through comparative analysis of the tweets via #meToo on Twitter versus the posts shared on the #meToo subreddit, this paper makes an initial attempt to assess public reactions and emotions. Though nearly equa…
Social media has become an indispensable part of the everyday lives of millions of people around the world. It provides a platform for expressing opinions and beliefs, communicated to a massive audience. However, this ease with which people can express themselves has also allowed for the large scale spread of propaganda and hate speech. To prevent violating the abuse policies of social media platforms and also to avoid detection by automatic systems like Google's Conversation AI, racists have begun to use a code (a movement termed Operation Google). This involves substituting references to com…
In this paper, we propose an approach for the detection of clickbait posts in online social media (OSM). Clickbait posts are short catchy phrases that attract a user's attention to click to an article. The approach is based on a machine learning (ML) classifier capable of distinguishing between clickbait and legitimate posts published in OSM. The suggested classifier is based on a variety of features, including image related features, linguistic analysis, and methods for abuser detection. In order to evaluate our method, we used two datasets provided by Clickbait Challenge 2017. The best perfo…
The intersection of social media, low-cost trading platforms, and naive investors has created an ideal situation for information-based market manipulations, especially pump&dumps. Manipulators accumulate small-cap stocks, disseminate false information on social media to inflate their price, and sell at the peak. We collect a dataset of stocks whose price and volume profiles have the characteristic shape of a pump&dump, and social media posts for those same stocks that match the timing of the initial price rises. From these we build predictive models for pump&dump events based on the language u…
Suicide is the 10th leading cause of death in the US and the 2nd leading cause of death among teenagers. Clinical and psychosocial factors contribute to suicide risk (SRFs), although documentation and self-expression of such factors in EHRs and social networks vary. This study investigates the degree of variance across EHRs and social networks. We performed subjective analysis of SRFs, such as self-harm, bullying, impulsivity, family violence/discord, using >13.8 Million clinical notes on 123,703 patients with mental health conditions. We clustered clinical notes using semantic embeddings unde…
One of the hallmarks of a free and fair society is the ability to conduct a peaceful and seamless transfer of power from one leader to another. Democratically, this is measured in a citizen population's trust in the electoral system of choosing a representative government. In view of the well documented issues of the 2016 US Presidential election, we conducted an in-depth analysis of the 2018 US Midterm elections looking specifically for voter fraud or suppression. The Midterm election occurs in the middle of a 4 year presidential term. For the 2018 midterms, 35 senators and all the 435 seats …
Interest surrounding cryptocurrencies, digital or virtual currencies that are used as a medium for financial transactions, has grown tremendously in recent years. The anonymity surrounding these currencies makes investors particularly susceptible to fraud---such as "pump and dump" scams---where the goal is to artificially inflate the perceived worth of a currency, luring victims into investing before the fraudsters can sell their holdings. Because of the speed and relative anonymity offered by social platforms such as Twitter and Telegram, social media has become a preferred platform for scamm…
Social media is both helpful and harmful to the work against sex trafficking. On one hand, social workers carefully use social media to support people experiencing sex trafficking. On the other hand, traffickers use social media to groom and recruit people into trafficking situations. There is the opportunity to use social media data to better provide support for people experiencing trafficking. While AI and Machine Learning (ML) have been used in work against sex trafficking, they predominantly focus on detecting Child Sexual Abuse Material. Work using social media data has not been done with…
In the context of Turkey's upcoming parliamentary and presidential elections ("seçim" in Turkish), social media is playing an important role in shaping public debate. The increasing engagement of citizens on social media platforms has led to the growing use of social media by political actors. It is of utmost importance to capture the upcoming Turkish elections, as social media is becoming an essential component of election propaganda, political debates, smear campaigns, and election manipulation by domestic and international actors. We provide a comprehensive dataset for social media research…
Since its inception, Facebook has become an integral part of the online social community. People rely on Facebook to make connections with others and build communities. As a result, it is paramount to protect the integrity of such a rapidly growing network in a fast and scalable manner. In this paper, we present our efforts to protect various social media entities at Facebook from people who try to abuse our platform. We present a novel Temporal Interaction EmbeddingS (TIES) model that is designed to capture rogue social interactions and flag them for further suitable actions. TIES is a superv…
Drug use by people is on the rise and is of great interest to public health agencies and law enforcement agencies. As found by the National Survey on Drug Use and Health, 20 million Americans aged 12 years or older consumed illicit drugs in the past few 30 days. Given their ubiquity in everyday life, drug abuse related studies have received much and constant attention. However, most of the existing studies rely on surveys. Surveys present a fair number of problems because of their nature. Surveys on sensitive topics such as illicit drug use may not be answered truthfully by the people taking t…
HP-BERT: A framework for longitudinal study of Hinduphobia on social media via language models
Transitional Artificial Intelligence Research Group, School of Mathematics and Statistics, UNSW Sydney（过渡人工智能研究组，数学与统计学学院，UNSW悉尼）；Centre for Artificial Intelligence and Innovation, Pingla Institute, Sydney, Australia（人工智能与创新中心，Pingla研究所，悉尼，澳大利亚）；Department of Data Science and Artificial Intelligence, IIIT Naya Raipur（数据科学与人工智能系，IIIT奈亚拉普尔）
Athens University of Economics and Business（雅典经济与商业大学）
In the era of social media and networking platforms, Twitter has been doomed for abuse and harassment toward users specifically women. Monitoring the contents including sexism and sexual harassment in traditional media is easier than monitoring on the online social media platforms like Twitter, because of the large amount of user generated content in these media. So, the research about the automated detection of content containing sexual or racist harassment is an important issue and could be the basis for removing that content or flagging it for human evaluation. Previous studies have been fo…
