Reza Zafarani

Sarcasm Detection on Twitter: A Behavioral Modeling Approach

Sarcasm is a nuanced form of language in which individuals state the opposite of what is implied. With this intentional ambiguity, sarcasm detection has always been a challenging task, even for humans. Current approaches to automatic sarcasm detection rely primarily on lexical and linguistic cues. This paper aims to address the difficult task of sarcasm detection on Twitter by leveraging behavioral traits intrinsic to users expressing sarcasm. We identify such traits using the users past tweets. We employ theories from behavioral and psychological studies to construct a behavioral modeling framework tuned for detecting sarcasm. We evaluate our framework and demonstrate its efficiency in identifying sarcastic tweets.

Maximizing benefits from crowdsourced data

Crowds of people can solve some problems faster than individuals or small groups. A crowd can also rapidly generate data about circumstances affecting the crowd itself. This crowdsourced data can be leveraged to benefit the crowd by providing information or solutions faster than traditional means. However, the crowdsourced data can hardly be used directly to yield usable information. Intelligently analyzing and processing crowdsourced information can help prepare data to maximize the usable information, thus returning the benefit to the crowd. This article highlights challenges and investigates opportunities associated with mining crowdsourced data to yield useful information, as well as details how crowdsource information and technologies can be used for response-coordination when needed, and finally suggests related areas for future research.

User Identification Across Social Media

People use various social media sites for different purposes. The information on each site is often partial. When sources of complementary information are integrated, a better profile of a user can be built. This profile can help improve online services such as advertising across sites. To integrate these sources of information, it is necessary to identify individuals across social media sites. This paper aims to address the cross-media user identification problem. We provide evidence on the existence of a mapping among identities of individuals across social media sites, study the feasibility of finding this mapping, and illustrate and develop means for finding this mapping. Our studies show that effective approaches that exploit information redundancies due to users’ unique behavioral patterns can be utilized to find such a mapping. This study paves the way for analysis and mining across social networking sites, and facilitates the creation of novel online services across sites. In particular, recommending friends and advertising across networks, analyzing information diffusion across sites, and studying specific user behavior such as user migration across sites in social media are one of the many areas that can benefit from the results of this study.

Sentiment propagation in social networks: a case study in livejournal

Social networking websites have facilitated a new style of communication through blogs, instant messaging, and various other techniques. Through collaboration, millions of users participate in millions of discussions every day. However, it is still difficult to determine the extent to which such discussions affect the emotions of the participants. We surmise that emotionally-oriented discussions may affect a given users general emotional bent and be reflected in other discussions he or she may initiate or participate in. It is in this way that emotion (or sentiment) may propagate through a network. In this paper, we analyze sentiment propagation in social networks, review the importance and challenges of such a study, and provide methodologies for measuring this kind of propagation. A case study has been conducted on a large dataset gathered from the LiveJournal social network. Experimental results are promising in revealing some aspects of the sentiment propagation taking place in social networks.

User Identity Linkage across Online Social Networks: A Review

The increasing popularity and diversity of social media sites has encouraged more and more people to participate on multiple online social networks to enjoy their services. Each user may create a user identity, which can includes profile, content, or network information, to represent his or her unique public figure in every social network. Thus, a fundamental question arises -- can we link user identities across online social networks? User identity linkage across online social networks is an emerging task in social media and has attracted increasing attention in recent years. Advancements in user identity linkage could potentially impact various domains such as recommendation and link prediction. Due to the unique characteristics of social network data, this problem faces tremendous challenges. To tackle these challenges, recent approaches generally consist of (1) extracting features and (2) constructing predictive models from a variety of perspectives. In this paper, we review key achievements of user identity linkage across online social networks including stateof- the-art algorithms, evaluation metrics, and representative datasets. We also discuss related research areas, open problems, and future research directions for user identity linkage across online social networks.

10 Bits of Surprise: Detecting Malicious Users with Minimum Information

Malicious users are a threat to many sites and defending against them demands innovative countermeasures. When malicious users join sites, they provide limited information about themselves. With this limited information, sites can find it difficult to distinguish between a malicious user and a normal user. In this study, we develop a methodology that identifies malicious users with limited information. As information provided by malicious users can vary, the proposed methodology utilizes minimum information to identify malicious users. It is shown that as little as 10 bits of information can help greatly in this challenging task. The experiments results verify that this methodology is effective in identifying malicious users in the realistic scenario of limited information availability.

Can reputation migrate? On the propagation of reputation in multi-context communities

As e-communities grow in both quality and quantity, their online users require more appropriate tools to suite their needs in such environments. Many such tools are not explicitly needed in real-world communities where humans directly interact with each other. Trust making and reputation ascription are among the most important examples of such tools. Humans often build trust relationships through interaction or recommendation, and are therefore able to ascribe relevant reputation to those they interact with. However, in online communities the process of trust making and reputation ascription is more complicated. In this paper, we address a special case of the trust making process where community users need to create bonds with those they have not encountered before. This is a common situation in websites such as amazon.com, ebay.com, epionions.com and many others. The model we propose is able to estimate the possible reputation of a given identity in a any new context by observing his/her behavior in other communities. Our proposed model employs Dempster-Shafer based valuation networks to develop a global reputation structure and performs a belief propagation technique to infer contextual reputation values. The preliminary evaluation of the proposed model on a dataset collected from epinions.com shows promising results.

A belief-theoretic reputation estimation model for multi-context communities

Online communities have grown to be an alternative form of communication for many people. This widespread growth and influence of the members of these communities in shaping the desire, line of thought and behavior of each other requires subtle mechanisms that are often easily attainable in face-to-face communications. In this paper, we address a special case of the trust-making process, where a person needs to make a judgment about the propositions, capabilities, or truthfulness of another community member where none of the community members has had any previous interaction with. Our proposed model estimates the possible reputation of a given identity in a new context by observing his/her behavior in the perspective of the other contexts of the community. This is most important for websites such as amazon.com, ebay.com, epinions.com, etc whose activities encompass multiple domains. Our proposed model employs Dempster-Shafer based valuation networks to represent a global reputation structure and performs a belief propagation technique to infer contextual reputation. The evaluation of the model on a dataset collected from epinions.com shows promising results

Users joining multiple sites

We conduct the first study on the variation of friendship and popularity across sites.As users join sites, their average number of friends converges to a value near 400.User popularity converges to a mean and it cannot be increased by joining new sites.Popularity patterns on previous sites can help determine user popularity on new sites. Our social media experience is no longer limited to a single site. We use different social media sites for different purposes and our information on each site is often partial. By collecting complementary information for the same individual across sites, one can better profile users. These profiles can help improve online services such as advertising or recommendation across sites. To combine complementary information across sites, it is critical to understand how information for the same individual varies across sites. In this study, we aim to understand how two fundamental properties of users vary across social media sites. First, we study how user friendship behavior varies across sites. Our findings show how friend distributions for individuals change as they join new sites. Next, we analyze how user popularity changes across sites as individuals join different sites. We evaluate our findings and demonstrate how our findings can be employed to predict how popular users are likely to be on new sites they join.

The good, the bad, and the ugly: uncovering novel research opportunities in social media mining

Big data is ubiquitous and can only become bigger, which challenges traditional data mining and machine learning methods. Social media is a new source of data that is significantly different from conventional ones. Social media data are mostly user-generated, and are big, linked, and heterogeneous. We present the good, the bad and the ugly associated with the multi-faceted social media data and exemplify the importance of some original problems with real-world examples. We discuss bias in social media data, evaluation dilemma, data reduction, inferring invisible information, and big-data paradox. We illuminate new opportunities of developing novel algorithms and tools for data science. In our endeavor of employing the good to tame the bad with the help of the ugly, we deepen the understanding of ever growing and continuously evolving data and create innovative solutions with interdisciplinary and collaborative research of data science.