Shivakant Mishra

Khaled Alanezi, Nuha Albadi, Omar Hammad et al.

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.

Upasana Dutta, Rhett Hanscom, Jason Shuo Zhang et al.

Social media platforms have been exploited to conduct election interference in recent years. In particular, the Russian-backed Internet Research Agency (IRA) has been identified as a key source of misinformation spread on Twitter prior to the 2016 U.S. presidential election. The goal of this research is to understand whether general Twitter users changed their behavior in the year following first contact from an IRA account. We compare the before and after behavior of contacted users to determine whether there were differences in their mean tweet count, the sentiment of their tweets, and the frequency and sentiment of tweets mentioning @realDonaldTrump or @HillaryClinton. Our results indicate that users overall exhibited statistically significant changes in behavior across most of these metrics, and that those users that engaged with the IRA generally showed greater changes in behavior.

Wanshan Yang, Ting Huang, Junlin Zeng et al.

In the free online game industry, churn prediction is an important research topic. Reducing the churn rate of a game significantly helps with the success of the game. Churn prediction helps a game operator identify possible churning players and keep them engaged in the game via appropriate operational strategies, marketing strategies, and/or incentives. Playtime related features are some of the widely used universal features for most churn prediction models. In this paper, we consider developing new universal features for churn predictions for long-term players based on players' playtime.

Nuha Albadi, Maram Kurdi, Shivakant Mishra

Arabic Twitter space is crawling with bots that fuel political feuds, spread misinformation, and proliferate sectarian rhetoric. While efforts have long existed to analyze and detect English bots, Arabic bot detection and characterization remains largely understudied. In this work, we contribute new insights into the role of bots in spreading religious hatred on Arabic Twitter and introduce a novel regression model that can accurately identify Arabic language bots. Our assessment shows that existing tools that are highly accurate in detecting English bots don't perform as well on Arabic bots. We identify the possible reasons for this poor performance, perform a thorough analysis of linguistic, content, behavioral and network features, and report on the most informative features that distinguish Arabic bots from humans as well as the differences between Arabic and English bots. Our results mark an important step toward understanding the behavior of malicious bots on Arabic Twitter and pave the way for a more effective Arabic bot detection tools.

Homa Hosseinmardi, Sabrina Arredondo Mattson, Rahat Ibn Rafiq et al.

Cyberbullying is a growing problem affecting more than half of all American teens. The main goal of this paper is to investigate fundamentally new approaches to understand and automatically detect and predict incidents of cyberbullying in Instagram, a media-based mobile social network. In this work, we have collected a sample data set consisting of Instagram images and their associated comments. We then designed a labeling study and employed human contributors at the crowd-sourced CrowdFlower website to label these media sessions for cyberbullying. A detailed analysis of the labeled data is then presented, including a study of relationships between cyberbullying and a host of features such as cyberaggression, profanity, social graph features, temporal commenting behavior, linguistic content, and image content. Using the labeled data, we further design and evaluate the performance of classifiers to automatically detect and pre- dict incidents of cyberbullying and cyberaggression.

Alireza Mahdian, Richard Han, Qin Lv et al.

This paper presents MyZone, a private online social network for relatively small, closely-knit communities. MyZone has three important distinguishing features. First, users keep the ownership of their data and have complete control over maintaining their privacy. Second, MyZone is free from any possibility of content censorship and is highly resilient to any single point of disconnection. Finally, MyZone minimizes deployment cost by minimizing its computation, storage and network bandwidth requirements. It incorporates both a P2P architecture and a centralized architecture in its design ensuring high availability, security and privacy. A prototype of MyZone was deployed over a period of 40 days with a membership of more than one hundred users. The paper provides a detailed evaluation of the results obtained from this deployment.

Xinyu Xing, Junho Ahn, Wenke Lee et al.

Recently, online video chat services are becoming increasingly popular. While experiencing tremendous growth, online video chat services have also become yet another spamming target. Unlike spam propagated via traditional medium like emails and social networks, we find that spam propagated via online video chat services is able to draw much larger attention from the users. We have conducted several experiments to investigate spam propagation on Chatroulette - the largest online video chat website. We have found that the largest spam campaign on online video chat websites is dating scams. Our study indicates that spam carrying dating or pharmacy scams have much higher clickthrough rates than email spam carrying the same content. In particular, dating scams reach a clickthrough rate of 14.97%. We also examined and analysed spam prevention mechanisms that online video chat websites have designed and implemented. Our study indicates that the prevention mechanisms either harm legitimate user experience or can be easily bypassed.