FRAppE: Detecting Malicious Facebook Applications

Online social media services like Facebook witness an exponential increase in user activity when an event takes place in the real world. This activity is a combination of good quality content like information, personal views, opinions, comments, as well as poor quality content like rumours, spam, and other malicious content. Even if the good quality content makes online social media very good source of information, uses of bad quality content can degrade user experience, and could have an inappropriate impact in the real world. It could also impact the enormous promptness, promptness, and reach of online social media services across the globe makes it very important to monitor these activities, and minimize the production and spread of bad quality content. Multiple studies in the past have analysed the content spread on social networks during real world events. However, little work has explored the Facebook social network. Two of the main reasons for the lack of studies on Facebook are the strict privacy settings, and limited amount of data available from Facebook, as compared to Twitter. With over 1 billion monthly active users, Facebook is about times bigger than its next biggest counterpart Twitter, and is currently, the largest online social network in the world. In this literature survey, we review the existing research work done on Facebook, and study the techniques used to identify and analyse poor quality content on Facebook, and other social networks. We also attempt to understand the limitations posed by Facebook in terms of availability of data for collection, and analysis, and try to understand if existing techniques can be used to identify and study poor quality content on Facebook

Information Trustworthiness and Information Adoption in Social Media Marketing: Contextualization of Ewom and Its Implications For Marketers

Social media platforms have exposed consumers to a large amount of either accurate information or misleading information. The quick spread of information through electronic word-of-mouth on social media networks has made it difficult for consumers to distinguish between marketer-generated content and user-generated content. This study aims to identify the factors that influence consumers when making purchasing decisions and to establish a comprehensive framework for consumers in the digital marketing. The study aimed to investigate how technology acceptance, electronic word-of-mouth (eWOM), and perceived risk affect information adoption by users in social media marketing. The study collected data from 213 social media users in Semarang via an online survey and used partial least squares structural equation modeling (PLS-SEM). The findings showed that information trustworthiness and information adoption were intermediaries between information quality, usefulness, perceived risk, argument quality, and information adoption. The study suggests that the quality and usefulness of the information are significant factors that affect the adoption of information. For social media marketers, providing high-quality and balancing useful information can increase consumer chances of adoption, thereby leading to purchase intention. The findings highlight for the marketers to ensure that the information provided is of high quality and relevant to the target audience. Keywords: digital marketing, social media, information adoption, electronic word-of- mouth, trus

Identification of the Emergent Leaders within a CSE Professional Development Program

The need for high quality, sustainable Computer Science Education (CSE) professional development (PD) at the grades K-12 level is essential to the success of the global CSE initiatives. This study investigates the use of Social Network Analysis (SNA) to identify emergent teacher leaders within a high quality CSE PD program. The CSE PD program was designed and implemented through collaboration between the computer science and teacher education units at a Midwestern metropolitan university in North America. A unique feature of this specific program is in the intentional development of a social network. This study discusses the importance of social networks, the development of social capital, and its impact on the sustainability of the goals of the CSE PD program. The role of emergent teacher leaders in the development of the social capital of the CSE PD cohort is investigated using SNA techniques. The cohort consisted of 16 in-service teachers in grades 6-12 representing seven districts and four distinct content areas. The instruments used involved a questionnaire and the results of a CSE PD program online course. The findings suggest a correlation between the emergent teacher leaders, the online course results, and the overall cohort social capital. Future uses of SNA within professional development programs are also discussed

Dynamics of Students’ Opinions in the Context of the Transition to Online Learning Based on Social Network Data

The article presents the results of the analysis of users’ sentiment in social networks, performed using big data tools. The research was aimed at developing the methodology, which enables to analyze the content of social networks, assess students’ attitude to the transition to online learning in conditions of COVID-19 pandemic, identify dynamics and main trends in student satisfaction with the quality of educational process. We explored about 2 million posts and comments posted in university social networks (more than 1000 university public pages) for the period from Sept 2020 to July 2021. Special attention was paid to the problems of communication between students and teachers, strategies to solve them, an emotional reaction. PolyAnalyst software was applied for data precleaning. It has been found that the main problem affecting the quality of education is a change in the mechanisms of interaction between students and teachers. Based on student publications in social networks, we have identified the strategies for adapting students to online learning. We came to a conclusion that teachers’ support of students is crucial in preventing and solving social and academic problems in conditions of online learning. One of the ways to improve interaction between students and teachers, raise students’ involvement is using discussion forums, chats in messengers for academic purposes, and providing teachers’ methodical support

Interactive Search and Exploration in Online Discussion Forums Using Multimodal Embeddings

In this paper we present a novel interactive multimodal learning system, which facilitates search and exploration in large networks of social multimedia users. It allows the analyst to identify and select users of interest, and to find similar users in an interactive learning setting. Our approach is based on novel multimodal representations of users, words and concepts, which we simultaneously learn by deploying a general-purpose neural embedding model. We show these representations to be useful not only for categorizing users, but also for automatically generating user and community profiles. Inspired by traditional summarization approaches, we create the profiles by selecting diverse and representative content from all available modalities, i.e. the text, image and user modality. The usefulness of the approach is evaluated using artificial actors, which simulate user behavior in a relevance feedback scenario. Multiple experiments were conducted in order to evaluate the quality of our multimodal representations, to compare different embedding strategies, and to determine the importance of different modalities. We demonstrate the capabilities of the proposed approach on two different multimedia collections originating from the violent online extremism forum Stormfront and the microblogging platform Twitter, which are particularly interesting due to the high semantic level of the discussions they feature

The Cost of Sharing Information in a Social World

With the increasing prevalence of large scale online social networks, the field has evolved from studying small scale networks and interactions to massive ones that encompass huge fractions of the world’s population. While many methods focus on techniques at scale applied to a single domain, methods that apply techniques across multiple domains are becoming increasingly important. These methods rely on understanding the complex relationships in the data. In the context of social networks, the big data available allows us to better model and analyze the flow of information within the network. The first part of this thesis discusses methods to more effectively learn and predict in a social network by leveraging information across multiple domains and types of data. We document a method to identify users from their access to content in a network and their click behavior. Even on a macro level, click behavior is often hard to obtain. We describe a technique to predict click behavior using other public information about the social network. Communication within a network inevitably has some bias that can be attributed to individual preferences and quality as well as the underlying structure of the network. The second part of the thesis characterizes the structural bias in a network by modeling the underlying information flow as a commodity of trade

Encouraging Social Innovation Through Capital: Using Technology to Address Barriers

Outlines how technology can help foster a robust capital market for public education innovation by improving content, linking technology with face-to-face networks, and streamlining transactions. Suggests steps for government, foundations, and developers

Automated Crowdturfing Attacks and Defenses in Online Review Systems

Malicious crowdsourcing forums are gaining traction as sources of spreading misinformation online, but are limited by the costs of hiring and managing human workers. In this paper, we identify a new class of attacks that leverage deep learning language models (Recurrent Neural Networks or RNNs) to automate the generation of fake online reviews for products and services. Not only are these attacks cheap and therefore more scalable, but they can control rate of content output to eliminate the signature burstiness that makes crowdsourced campaigns easy to detect. Using Yelp reviews as an example platform, we show how a two phased review generation and customization attack can produce reviews that are indistinguishable by state-of-the-art statistical detectors. We conduct a survey-based user study to show these reviews not only evade human detection, but also score high on "usefulness" metrics by users. Finally, we develop novel automated defenses against these attacks, by leveraging the lossy transformation introduced by the RNN training and generation cycle. We consider countermeasures against our mechanisms, show that they produce unattractive cost-benefit tradeoffs for attackers, and that they can be further curtailed by simple constraints imposed by online service providers