An Enhanced Scammer Detection Model for Online Social Network Frauds Using Machine Learning

The prevalence of online social networking increase in the risk of social network scams or fraud. Scammers often create fake profiles to trick unsuspecting users into fraudulent activities. Therefore, it is important to be able to identify these scammer profiles and prevent fraud such as dating scams, compromised accounts, and fake profiles. This study proposes an enhanced scammer detection model that utilizes user profile attributes and images to identify scammer profiles in online social networks. The approach involves preprocessing user profile data, extracting features, and machine learning algorithms for classification. The system was tested on a dataset created specifically for this study and was found to have an accuracy rate of 94.50% with low false-positive rates. The proposed approach aims to detect scammer profiles early on to prevent online social network fraud and ensure a safer environment for society and women’s safety

SpADe: Multi-Stage Spam Account Detection for Online Social Networks

In recent years, Online Social Networks (OSNs) have radically changed the way people communicate. The most widely used platforms, such as Facebook, Youtube, and Instagram, claim more than one billion monthly active users each. Beyond these, news-oriented micro-blogging services, e.g., Twitter, are daily accessed by more than 120 million users sharing contents from all over the world. Unfortunately, legitimate users of the OSNs are mixed with malicious ones, which are interested in spreading unwanted, misleading, harmful, or discriminatory content. Spam detection in OSNs is generally approached by considering the characteristics of the account under analysis, its connection with the rest of the network, as well as data and metadata representing the content shared. However, obtaining all this information can be computationally expensive, or even unfeasible, on massive networks. Driven by these motivations, in this paper we propose SpADe, a multi-stage Spam Account Detection algorithm with reject option, whose purpose is to exploit less costly features at the early stages, while progressively extracting more complex information only for those accounts that are difficult to classify. Experimental evaluation shows the effectiveness of the proposed algorithm compared to single-stage approaches, which are much more complex in terms of features processing and classification time

Public perceptions towards MOOCs on social media: an alternative perspective to understand personal learning experiences of MOOCs

This study attempted to assess public perceptions of and interest in MOOCs by examining how Weibo increases public discussion of MOOCs as well as by interpreting how individual learners talk about their learning experiences. Over 4,000 microblog posts were collected and analysed between 2013 and 2018. The findings showed that Weibo is used as a public service medium to augment the publicity of the MOOC movement and increase the accessibility of MOOC portals. The results also demonstrated that Weibo acts as a space for learners to share their personal learning experiences, which reflect aspects of autonomous, self-regulated, interactive and cooperative learning. By posting on Weibo, close peer connections and learning groups were established to encourage MOOC learning. This study’s findings further the scholarly understanding of how MOOCs are discussed on social media and address an important gap around what is known in one of the largest and most under-researched sites of informal online learning