Assessing the Barriers and Risks to Private Sector Participation in Infrastructure Construction Projects in Developing Countries of Middle East

In developing countries, governments are often unable to implement urban infrastructure construction projects (UICPs) on their own, mainly due to budget and financial resource limitations. The participation of the private sector, through public–private partnerships (PPPs), has been considered as an alternative effective method for increasing the efficiency and productivity of urban infrastructure development. However, in many developing countries such as those situated in the Middle East, attracting private sector investments for UICPs uncovers profound challenges that have not ever been comprehensively accounted for and prioritized. To fill this knowledge gap, this study seeks to determine and prioritize the major barriers and risks faced by governments and urban managers in attracting private sector investments through the PPP schemes launched by developing countries in the Middle East. Based on a Delphi study conducted in Iran as an example, the opinions of 60 UICPs experts in both the public and private sectors were collected and analyzed. Results show that technical and organizational barriers and risks were perceived as the most important to private sector participation, followed by economic and financial barriers and risks, and then political and legal barriers and risk

Detecting Aggressors and Bullies on Twitter

Online social networks constitute an integral part of people's every day social activity and the existence of aggressive and bullying phenomena in such spaces is inevitable. In this work, we analyze user behavior on Twitter in an effort to detect cyberbullies and cuber-aggressors by considering specific attributes of their online activity using machine learning classifiers

Identification and Prioritization of Critical Risk Factors of Commercial and Recreational Complex Building Projects: A Delphi Study Using the TOPSIS Method

Construction development of Commercial and Recreational Complex Building Projects (CRCBPs) is one of the community needs of many developing countries. Since the implementation of these projects is usually very costly, identifying and evaluating their Critical Risk Factors (CRFs) are of significant importance. Therefore, the current study aims to identify and prioritize CRFs of CRCBPs in the Iranian context. A descriptive-survey method was used in this research; the statistical population, selected based on the purposive sampling method, includes 30 construction experts with hands-on experience in CRCBPs. A questionnaire related to the risk identification stage was developed based on a detailed study of the research literature and also using the Delphi survey method; 82 various risks were finally identified. In order to confirm the opinions of experts in identifying the potential risks, Kendall’s coefficient of concordance was used. In the first stage of data analysis, qualitative evaluation was performed by calculating the severity of risk effect and determining the cumulative risk index, based on which 25 CRFs of CRCBPs were identified for more accurate evaluation. At this stage, the identified CRFs were evaluated based on multi-criteria decision-making techniques and using the TOPSIS technique. Results show that the ten CRFs of CRCBPs are external threats from international relations, exchange rate changes, bank interest rate fluctuations, traffic licenses, access to skilled labor, changes in regional regulations, the condition of adjacent buildings, fluctuations and changes in inflation, failure to select a suitable and qualified consultant, and employer’s previous experiences and records. Obviously, the current study’s results and findings can be considered by CRCBPs in both the private and public sectors for proper effective risk identification, evaluation, and mitigatio

Measuring #GamerGate: A Tale of Hate, Sexism, and Bullying

Over the past few years, online aggression and abusive behaviors have occurred in many different forms and on a variety of platforms. In extreme cases, these incidents have evolved into hate, discrimination, and bullying, and even materialized into real-world threats and attacks against individuals or groups. In this paper, we study the Gamergate controversy. Started in August 2014 in the online gaming world, it quickly spread across various social networking platforms, ultimately leading to many incidents of cyberbullying and cyberaggression. We focus on Twitter, presenting a measurement study of a dataset of 340k unique users and 1.6M tweets to study the properties of these users, the content they post, and how they differ from random Twitter users. We find that users involved in this "Twitter war" tend to have more friends and followers, are generally more engaged and post tweets with negative sentiment, less joy, and more hate than random users. We also perform preliminary measurements on how the Twitter suspension mechanism deals with such abusive behaviors. While we focus on Gamergate, our methodology to collect and analyze tweets related to aggressive and bullying activities is of independent interest

Mean birds: Detecting aggression and bullying on Twitter

In recent years, bullying and aggression against social media users have grown significantly, causing serious consequences to victims of all demographics. Nowadays, cyberbullying affects more than half of young social media users worldwide, suffering from prolonged and/or coordinated digital harassment. Also, tools and technologies geared to understand and mitigate it are scarce and mostly ineffective. In this paper, we present a principled and scalable approach to detect bullying and aggressive behavior on Twitter. We propose a robust methodology for extracting text, user, and network-based attributes, studying the properties of bullies and aggressors, and what features distinguish them from regular users. We find that bullies post less, participate in fewer online communities, and are less popular than normal users. Aggressors are relatively popular and tend to include more negativity in their posts. We evaluate our methodology using a corpus of 1.6M tweets posted over 3 months, and show that machine learning classification algorithms can accurately detect users exhibiting bullying and aggressive behavior, with over 90% AUC

Flexible and Robust Privacy-Preserving Implicit Authentication

Implicit authentication consists of a server authenticating a user based on the user's usage profile, instead of/in addition to relying on something the user explicitly knows (passwords, private keys, etc.). While implicit authentication makes identity theft by third parties more difficult, it requires the server to learn and store the user's usage profile. Recently, the first privacy-preserving implicit authentication system was presented, in which the server does not learn the user's profile. It uses an ad hoc two-party computation protocol to compare the user's fresh sampled features against an encrypted stored user's profile. The protocol requires storing the usage profile and comparing against it using two different cryptosystems, one of them order-preserving; furthermore, features must be numerical. We present here a simpler protocol based on set intersection that has the advantages of: i) requiring only one cryptosystem; ii) not leaking the relative order of fresh feature samples; iii) being able to deal with any type of features (numerical or non-numerical). Keywords: Privacy-preserving implicit authentication, privacy-preserving set intersection, implicit authentication, active authentication, transparent authentication, risk mitigation, data brokers.Comment: IFIP SEC 2015-Intl. Information Security and Privacy Conference, May 26-28, 2015, IFIP AICT, Springer, to appea

"I'm a Professor, which isn't usually a dangerous job": Internet-facilitated Harassment and Its Impact on Researchers

While the Internet has dramatically increased the exposure that research can receive, it has also facilitated harassment against scholars. To understand the impact that these attacks can have on the work of researchers, we perform a series of systematic interviews with researchers including academics, journalists, and activists, who have experienced targeted, Internet-facilitated harassment. We provide a framework for understanding the types of harassers that target researchers, the harassment that ensues, and the personal and professional impact on individuals and academic freedom. We then study preventative and remedial strategies available, and the institutions that prevent some of these strategies from being more effective. Finally, we discuss the ethical structures that could facilitate more equitable access to participating in research without serious personal suffering

Detecting cyberbullying and cyberaggression in social media

Cyberbullying and cyberaggression are increasingly worrisome phenomena affecting people across all demographics. More than half of young social media users worldwide have been exposed to such prolonged and/or coordinated digital harassment. Victims can experience a wide range of emotions, with negative consequences such as embarrassment, depression, isolation from other community members, which embed the risk to lead to even more critical consequences, such as suicide attempts. In this work, we take the first concrete steps to understand the characteristics of abusive behavior in Twitter, one of today’s largest social media platforms. We analyze 1.2 million users and 2.1 million tweets, comparing users participating in discussions around seemingly normal topics like the NBA, to those more likely to be hate-related, such as the Gamergate controversy, or the gender pay inequality at the BBC station. We also explore specific manifestations of abusive behavior, i.e., cyberbullying and cyberaggression, in one of the hate-related communities (Gamergate). We present a robust methodology to distinguish bullies and aggressors from normal Twitter users by considering text, user, and network-based attributes. Using various state-of-the-art machine-learning algorithms, we classify these accounts with over 90% accuracy and AUC. Finally, we discuss the current status of Twitter user accounts marked as abusive by our methodology and study the performance of potential mechanisms that can be used by Twitter to suspend users in the future

Understanding the Use of Fauxtography on Social Media

Despite the influence that image-based communication has on online discourse, the role played by images in disinformation is still not well understood. In this paper, we present the first large-scale study of fauxtography, analyzing the use of manipulated or misleading images in news discussion on online communities. First, we develop a computational pipeline geared to detect fauxtography, and identify over 61k instances of fauxtography discussed on Twitter, 4chan, and Reddit. Then, we study how posting fauxtography affects engagement of posts on social media, finding that posts containing it receive more interactions in the form of re-shares, likes, and comments. Finally, we show that fauxtography images are often turned into memes by Web communities. Our findings show that effective mitigation against disinformation need to take images into account, and highlight a number of challenges in dealing with image-based disinformation