Planned disruptions such as highway constructions are commonplace nowadays and the communities living near these disruptions generally tend to be environmental justice communities---low socio-economic status with disproportionately high and adverse human health and environmental effects. A major concern is that such activities negatively impact people's well-being by disrupting their daily commutes via frequent road closures and increased dust & air pollution. This paper addresses this concern by developing a personalized navigation service called PureNav to mitigate the negative impacts of disruptions in daily commutes on people's well-being. PureNav has been designed using active engagement with four environmental justice communities affected by major highway construction. It has been deployed in the real world among the members of the four communities, and a detailed analysis of the data collected from this deployment as well as surveys show that PureNav is potentially useful in improving people's wellbeing. The paper describes the design, implementation, and evaluation of PureNav, and offers suggestions for further improving its efficacy.

Online reviews have become essential for users to make informed decisions in everyday tasks ranging from planning summer vacations to purchasing groceries and making financial investments. A key problem in using online reviews is the overabundance of online that overwhelms the users. As a result, recommendation systems for providing helpfulness of reviews are being developed. This paper argues that cultural background is an important feature that impacts the nature of a review written by the user, and must be considered as a feature in assessing the helpfulness of online reviews. The paper provides an in-depth study of differences in online reviews written by users from different cultural backgrounds and how incorporating culture as a feature can lead to better review helpfulness recommendations. In particular, we analyze online reviews originating from two distinct cultural spheres, namely Arabic and Western cultures, for two different products, hotels and books. Our analysis demonstrates that the nature of reviews written by users differs based on their cultural backgrounds and that this difference varies based on the specific product being reviewed. Finally, we have developed six different review helpfulness recommendation models that demonstrate that taking culture into account leads to better recommendations.

The unprecedented health situation in the year 2020 and to some extent 2021 has forced most businesses to operate online with people working from home (WFH). Like almost all countries in the world, Saudi Arabia has suffered from the shocking unstable health situation facing the COVID-19 pandemic. This study investigates different WFH impressions and behaviors that Saudi employees have built during the pandemic and how that has changed over time. We have conducted surveys in two different phases among Saudi employees that have come from varied personal and job-related demographics, including different gender, marital status, cities, managerial roles, job sectors and company sizes. Our data provides a good comprehensive coverage along different demographics. Key findings includes that for 75% of the people it was a brand new experience especially for big companies employees, people's performance and satisfaction depended on the sector that they work for and their marital status, while life work split was the top challenge and flexibility was the top advantage.

Social media platforms have been exploited to disseminate misinformation in recent years. The widespread online misinformation has been shown to affect users' beliefs and is connected to social impact such as polarization. In this work, we focus on misinformation's impact on specific user behavior and aim to understand whether general Twitter users changed their behavior after being exposed to misinformation. We compare the before and after behavior of exposed users to determine whether the frequency of the tweets they posted, or the sentiment of their tweets underwent any significant change. Our results indicate that users overall exhibited statistically significant changes in behavior across some of these metrics. Through language distance analysis, we show that exposed users were already different from baseline users before the exposure. We also study the characteristics of two specific user groups, multi-exposure and extreme change groups, which were potentially highly impacted. Finally, we study if the changes in the behavior of the users after exposure to misinformation tweets vary based on the number of their followers or the number of followers of the tweet authors, and find that their behavioral changes are all similar.

YouTube strives to moderate its content by censoring, demonetizing or removing videos that allegedly violate their community guidelines. Such strategies, especially if seen as unjust by the affected users, could be met with resentment, anger, and in some cases, violence. In addition to YouTube removing videos, uploaders sometimes delete their videos for a variety of reasons such as paraphrasing or preserving online self-image. In this paper, we provide a detailed analysis of deleted/removed videos on YouTube. To do this, we tracked over 73,000 recent YouTube videos for one week and identified those that got deleted or removed. We have then conducted a large-scale analysis of this data and reported on the most informative features that distinguish deleted/removed videos from the ones that remain available. Based on our analysis, we have developed machine learning prediction models that predict videos that will get deleted/removed at different stages of a video's lifetime, viz., at the time of posting, and after up to seven days have elapsed. Our findings indicate that we can predict video deletion/removal with high accuracy even at the time of posting-a strategy that could help users perceive the removal of their videos as fair as well as reduce public and moderators exposure to problematic videos.

Internet users are routinely exposed to fake news in their social media feeds. The main goal of this paper is to identify the factors readers consider important in discriminating against fake news from true news when reading an online news article. We design and conduct three surveys using Amazon Mechanical Turk to identify the top factors and rate them under diverse scenarios. Our results suggest that people perceive news Source and Content to be the most important factors, in general, to distinguish fake news from true news, however, their importance reduces in practice when people actually read a news article. Furthermore, the importance of different factors in the credibility determination of a news article varies with people's political leanings. Our work is the first of its kind and offers new insights into how people determine the legitimacy of online news articles.

In the free online game industry, in-game purchase prediction is an important research topic. On the one hand, knowing the players who are likely or unlikely to pay for the game can help the development of corresponding marketing strategies and incentives. On the other hand, the result of purchase prediction indicates the status of the game operation status and helps the game company allocate a reasonable amount of resources for the game. In this paper, we focus on predicting the purchase probability of paying players and finding important features. We consider two types of features related to players’ playing and purchase behaviors. Then, based on these features we proposed, we use two different models to predict the survival probability for players and analyze the feature importance.

Religious hate speech in the Arabic Twittersphere is a notable problem that requires developing automated tools to detect messages that use inflammatory sectarian language to promote hatred and violence against people on the basis of religious affiliation. Distinguishing hate speech from other profane and vulgar language is quite a challenging task that requires deep linguistic analysis. The richness of the Arabic morphology and the limited available resources for the Arabic language make this task even more challenging. To the best of our knowledge, this paper is the first to address the problem of identifying speech promoting religious hatred in the Arabic Twitter. In this work, we describe how we created the first publicly available Arabic dataset annotated for the task of religious hate speech detection and the first Arabic lexicon consisting of terms commonly found in religious discussions along with scores representing their polarity and strength. We then developed various classification models using lexicon-based, n-gram-based, and deep-learning-based approaches. A detailed comparison of the performance of different models on a completely new unseen dataset is then presented. We find that a simple Recurrent Neural Network (RNN) architecture with Gated Recurrent Units (GRU) and pre-trained word embeddings can adequately detect religious hate speech with 0.84 Area Under the Receiver Operating Characteristic curve (AUROC).

User emotions are important contextual features in building context-aware pervasive applications. In this paper, we explore the question of whether it is possible to predict user emotions from their smartphone activities. To get the ground truth data, we have built an Android app that collects user emotions along with a number of features including their current location, activity they are engaged in, and smartphones apps they are currently running. We deployed this app for over a period of three months and collected a large amount of useful user data. We describe the details of this data in terms of statistics and user behaviors, provide a detailed analysis in terms of correlations between user emotions and other features, and finally build classifiers to predict user emotions. Performance of these classifiers is quite promising with high accuracy. We describe the details of these classifiers along with the results.

Cyberbullying is a major problem affecting more than half of all American teens. Prior work has largely focused on detecting cyberbullying after the fact. In this paper, we investigate the prediction of cyberbullying incidents in Instagram, a popular media-based social network. The novelty of this work is building a predictor that can anticipate the occurrence of cyberbullying incidents before they happen. The Instagram media-based social network is well-suited to such prediction since there is an initial posting of an image typically with an associated text caption, followed later by the text comments that form the basis of a specific cyberbullying incident. We extract several important features from the initial posting data for automated cyberbullying prediction, including profanity and linguistic content of the text caption, image content, as well as social graph parameters and temporal content behavior. Evaluations using a real-world Instagram dataset demonstrate that our method achieves high performance in predicting the occurrence of cyberbullying incidents.

The theme of cybersafety is an important emerging research topic on the Internet that manifests itself daily as users navigate the Web and networked applications. Examples of cybersafety issues include cyberbullying, cyberthreats, recruiting minors via Internet services for nefarious purposes, using deceptive means to dupe vulnerable populations, exhibiting misbehaving behaviors such as using profanity or flashing in online video chats, and many others. These issues have a direct negative impact on the social, psychological and in some cases physical well-being of the end users. An important characteristic of these issues is that they fall in a grey legal area, where perpetrators may claim freedom of speech or rights to free expression despite causing harm. The main goal of this inaugural workshop on cybersafety is to bring together the researchers and practitioners from academia, industry, government and research labs working in the area of cybersafety to discuss the unique challenges in addressing various cybersafety issues and to share experiences, solutions, tools, and techniques. The focus is on the detection, prevention and mitigation of various cybersafety issues, as well as education and promoting safe practices.

As online social networks have grown in popularity, teenage users have become increasingly exposed to the threats of cyberbullying. The primary goal of this research paper is to investigate cyberbullying behaviors in Vine, a mobile based video-sharing online social network, and design novel approaches to automatically detect instances of cyberbullying over Vine media sessions. We first collect a set of Vine video sessions and use CrowdFlower, a crowd-sourced website, to label the media sessions for cyberbullying and cyberaggression. We then perform a detailed analysis of cyberbullying behavior in Vine. Based on the labeled data, we design a classifier to detect instances of cyberbullying and evaluate the performance of that classifier.

Usage of social networks has exploded over the past decade or so. Users now routinely share their thought, opinions, feelings as well as their daily activities on various social networks. An interesting consequence of this explosive usage of social networks is that it is possible to glean the current mood and emotion of a user from his or her social network postings. A question that arises in this context is: Can we use any differentiating features exhibited by people on their online social activities to build appropriate classifiers that can identify the positivity or negativity of users with high accuracy and low false positive and negative rates?

Cyberbullying has emerged as an important and growing social problem, wherein people use online social networks and mobile phones to bully victims with offensive text, images, audio and video on a 24/7 basis. This paper studies negative user behavior in the Ask.fm social network, a popular new site that has led to many cases of cyberbullying, some leading to suicidal behavior.We examine the occurrence of negative words in Ask.fm's question+answer profiles along with the social network of “likes” of questions+answers. We also examine properties of users with “cutting” behavior in this social network.

Effect of mood and emotion on a person's behavior and his/her interactions with other people has been studied for a long time. Positivity and negativity of a person are two important attributes of emotion and mood. Social media is a very important platform from which we can glean the positivity and negativity attributes of a user based on his/her message postings and interactions with other users. In this paper, we study and analyze a Twitter dataset of more than 130,000 users to understand the nature of their positivity and negativity attributes. We measure behavioral attributes by sentiment analysis relating to social personal concern and psychological process. We observe that social media contains useful behavioral cues to classify users into positive and negative groups based on network density and degree of social activity either in information sharing or emotional interaction and social awareness. We believe that our findings will be useful in developing tools for predicting positive and negative users and help provide the best recommendation towards helping negative users through online social media.

The need for highly scalable and accurate detection and filtering of misbehaving users and obscene content in online video chat services has grown as the popularity of these services has exploded in popularity. This is a challenging problem because processing large amounts of video is compute intensive, decisions about whether a user is misbehaving or not must be made online and quickly, and moreover these video chats are characterized by low quality video, poorly lit scenes, diversity of users and their behaviors, diversity of the content, and typically short sessions. This paper presents EMeralD, a highly scalable system for accurately detecting and filtering misbehaving users in online video chat applications. EMeralD substantially improves upon the state-of-the-art filtering mechanisms by achieving much lower computational cost and higher accuracy. We demonstrate EMeralD's improvement via experimental evaluations on real-world data sets obtained from Chatroulette.com.

Online video chat services, such as Chatroulette, Omegle, and vChatter are becoming increasingly popular and have attracted millions of users. One critical problem encountered in such applications is the presence of misbehaving users ("flashers") and obscene content. Automatically filtering out obscene content from these systems in an efficient manner poses a difficult challenge. This paper presents a novel Fine-Grained Cascaded (FGC) classification solution that significantly speeds up the compute-intensive process of classifying misbehaving users by dividing image feature extraction into multiple stages and filtering out easily classified images in earlier stages, thus saving unnecessary computation costs of feature extraction in later stages. Our work is further enhanced by integrating new webcam-related contextual information (illumination and color) into the classification process, and a 2-stage soft margin SVM algorithm for combining multiple features. Evaluation results using real-world data set obtained from Chatroulette show that the proposed FGC based classification solution significantly outperforms state-of-the-art techniques.

Online video chat services such as Chatroulette, Omegle, and vChatter that randomly match pairs of users in video chat sessions are fast becoming very popular, with over a million users per month in the case of Chatroulette. A key problem encountered in such systems is the presence of flashers and obscene content. This problem is especially acute given the presence of underage minors in such systems. This paper presents SafeVchat, a novel solution to the problem of flasher detection that employs an array of image detection algorithms. A key contribution of the paper concerns how the results of the individual detectors are fused together into an overall decision classifying the user as misbehaving or not, based on Dempster-Shafer Theory. The paper introduces a novel, motion-based skin detection method that achieves significantly higher recall and better precision. The proposed methods have been evaluated over real-world data and image traces obtained from Chatroulette.com.