Reza Farahbakhsh

Analysis of publicly disclosed information in Facebook profiles

Facebook, the most popular Online social network is a virtual environment where users share information and are in contact with friends. Apart from many useful aspects, there is a large amount of personal and sensitive information publicly available that is accessible to external entities/users. In this paper we study the public exposure of Facebook profile attributes to understand what type of attributes are considered more sensitive by Facebook users in terms of privacy, and thus are rarely disclosed, and which attributes are available in most Facebook profiles. Furthermore, we also analyze the public exposure of Facebook users by accounting the number of attributes that users make publicly available on average. To complete our analysis we have crawled the profile information of 479K randomly selected Facebook users. Finally, in order to demonstrate the utility of the publicly available information in Facebook profiles we show in this paper three case studies. The first one carries out a gender-based analysis to understand whether men or women share more or less information. The second case study depicts the age distribution of Facebook users. The last case study uses data inferred from Facebook profiles to map the distribution of worldwide population across cities according to its size.

Stweeler: A Framework for Twitter Bot Analysis

The WWW has seen a massive growth in variety and usage of OSNs. The rising population of users on Twitter and its open nature has made it an ideal platform for various kinds of opportunistic pursuits, such as news and emergency communication, business promotion, political campaigning, spamming and spreading malicious content. Most of these opportunistic pursuits are exploited through automated programs, known as bots. In this study we propose a framework (Stweeler) to study bot impact and influence on Twitter from systems and social media perspectives.

CSD: A multi-user similarity metric for community recommendation in online social networks

Abstract Communities are basic components in networks. As a promising social application, community recommendation selects a few items (e.g., movies and books) to recommend to a group of users. It usually achieves higher recommendation precision if the users share more interests; whereas, in plenty of communities (e.g., families, work groups), the users often share few. With billions of communities in online social networks, quickly selecting the communities where the members are similar in interests is a prerequisite for community recommendation. To this end, we propose an easy-to-compute metric, Community Similarity Degree (CSD), to estimate the degree of interest similarity among multiple users in a community. Based on 3460 emulated Facebook communities, we conduct extensive empirical studies to reveal the characteristics of CSD and validate the effectiveness of CSD. In particular, we demonstrate that selecting communities with larger CSD can achieve higher recommendation precision. In addition, we verify the computation efficiency of CSD: it costs less than 1 hour to calculate CSD for over 1 million of communities. Finally, we draw insights about feasible extensions to the definition of CSD, and point out the practical uses of CSD in a variety of applications other than community recommendation.

NetSpam: A Network-Based Spam Detection Framework for Reviews in Online Social Media

Nowadays, a big part of people rely on available content in social media in their decisions (e.g., reviews and feedback on a topic or product). The possibility that anybody can leave a review provides a golden opportunity for spammers to write spam reviews about products and services for different interests. Identifying these spammers and the spam content is a hot topic of research, and although a considerable number of studies have been done recently toward this end, but so far the methodologies put forth still barely detect spam reviews, and none of them show the importance of each extracted feature type. In this paper, we propose a novel framework, named NetSpam, which utilizes spam features for modeling review data sets as heterogeneous information networks to map spam detection procedure into a classification problem in such networks. Using the importance of spam features helps us to obtain better results in terms of different metrics experimented on real-world review data sets from Yelp and Amazon Web sites. The results show that NetSpam outperforms the existing methods and among four categories of features, including review-behavioral, user-behavioral, review-linguistic, and user-linguistic, the first type of features performs better than the other categories.

Do Bots impact Twitter activity

The WWW has seen massive growth in population of automated programs (bots) for a variety of exploits on online social networks (OSNs). In this paper we extend on our previous work to study the affects of bots on Twitter. By setting up a bot account on Twitter and conducting analysis on a click logs dataset from our web server, we show that despite bots being in smaller numbers, they exercise a profound impact on content popularity and activity on Twitter.

A trust model for data sharing in smart cities

The data generated by the devices and existing infrastructure in the Internet of Things (IoT) should be shared among applications. However, data sharing in the IoT can only reach its full potential when multiple participants contribute their data, for example when people are able to use their smartphone sensors for this purpose. We believe that each step, from sensing the data to the actionable knowledge, requires trust-enabled mechanisms to facilitate data exchange, such as data perception trust, trustworthy data mining, and reasoning with trust related policies. The absence of trust could affect the acceptance of sharing data in smart cities. In this study, we focus on data usage transparency and accountability and propose a trust model for data sharing in smart cities, including system architecture for trust-based data sharing, data semantic and abstraction models, and a mechanism to enhance transparency and accountability for data usage. We apply semantic technology and defeasible reasoning with trust data usage policies. We built a prototype based on an air pollution monitoring use case and utilized it to evaluate the performance of our solution.

Characterization of Cross-posting Activity for Professional Users Across Major OSNs

Online Social Networks (OSNs) are being intensively used by professional users (e.g., companies, politician, athletes, celebrities, etc) in order to interact with a huge amount of regular OSN users with different purposes (marketing campaigns, customer feedback, public reputation, etc). Hence, due to the large catalog of existing OSNs, professional users usually count with OSN accounts in different systems. In this context an interesting question is whether professional users publish the same information across their OSN accounts, or actually they use different OSNs in a different manner. We define as cross-posting activity the action of publishing the same information in two or more OSNs. In this paper we aim at characterizing the cross-posting activity of professional OSN users across three major OSNs, Facebook, Twitter and Google+. To achieve this goal we perform a large-scale measurement-based analysis across more than 2M posts collected from 616 professional users with active accounts in the three referred OSNs.

Usage Control for Data Handling in Smart Cities

Data in smart cities is commonly generated by a large variety of participants including institutional actors, equipment manufacturers, network operators, infrastructure providers, service providers, and end users. This data potentially undergoes several transformations such as aggregation and/or composition before finally being consumed. In this context of sharing data between diverse consumers, it is essential to provide the data producers the means by which they can exercise control over how and by whom the data is used. To date, usage control has received attention in the domains of the web and social networks, in terms of confidentiality, privacy and access control aspects. However, it has not yet been fully applied in a rigorous manner in the context of smart cites. In this paper we study usage control with the goal to address the problem of providing stakeholders more control over their data and enforcing accountable management of such data. We first propose a new data usage policy, called DUPO, which captures the diversity of obligations and constraints resulting from the usage control requirements for smart cities. Next, we apply a defeasible logic based approach on DUPO to formally define rule language, solve rule conflicts, and elaborate reasoning. We then introduce the data handling mechanism, which provides useful functionality to process consumers request, ensuring the accountability of the policy enforcement, and traceability of the data usage. To this end we benefit from SPINdle reasoner to implement the proposed usage control module covered main functionalities of the mechanism.

The Implications of Twitterbot Generated Data Traffic on Networked Systems

The explosion of bots on the Web brings an unprecedented increase in traffic from non-human sources. This work studies bot traffic on Twitter, finding that almost 50% of traffic is generated and propagated by a rapidly growing bot population -- a major concern for networked systems in the future.

Characterization of cross-posting activity for professional users across Facebook, Twitter and Google+

Professional players in social media (e.g., big companies, politician, athletes, celebrities, etc) are intensively using Online Social Networks (OSNs) in order to interact with a huge amount of regular OSN users with different purposes (marketing campaigns, customer feedback, public reputation improvement, etc). Hence, due to the large catalog of existing OSNs, professional players usually count with OSN accounts in different systems. In this context, an interesting question is whether professional users publish the same information across their OSN accounts, or actually they use different OSNs in a different manner. We define as cross-posting activity the action of publishing the same information in two or more OSNs. This paper aims at characterizing the cross-posting activity of professional users across three major OSNs, Facebook, Twitter and Google+. To this end, we perform a large-scale measurement-based analysis across more than 2M posts collected from 616 professional users with active accounts in the three referred OSNs. Then we characterize the phenomenon of cross-posting and analyse the behavioural patterns based on the identified characteristics.