Typification of impersonated accounts on Instagram

International audienceFake accounts and Impersonators on Online Social Networks such as Instagram are turning difficulties for society. This has attended to an increasing interest in detecting fake profiles and investigating their behaviours. Questions like who are impersonators? what are their characteristics? and are they bots? will arise. To answer, we begin this research by collecting data from three important communities on Instagram including “Politician”, “News agency”, and “Sports star”. Inside each community, four verified top accounts are picked. Based on the users who reacted to their published posts, we detect 4K impersonators [1]. Then we employed well-known clustering methods to distribute impersonators into separated clusters to observe obscure behaviours and unusual profile characteristics. We also studied the cross-group analysis of clusters inside each community to explore engagements. Finally, we conclude the study by providing a complete investigation of the bot-like cluste

Exploring machine learning techniques for fake profile detection in online social networks

The online social network is the largest network, more than 4 billion users use social media and with its rapid growth, the risk of maintaining the integrity of data has tremendously increased. There are several kinds of security challenges in online social networks (OSNs). Many abominable behaviors try to hack social sites and misuse the data available on these sites. Therefore, protection against such behaviors has become an essential requirement. Though there are many types of security threats in online social networks but, one of the significant threats is the fake profile. Fake profiles are created intentionally with certain motives, and such profiles may be targeted to steal or acquire sensitive information and/or spread rumors on online social networks with specific motives. Fake profiles are primarily used to steal or extract information by means of friendly interaction online and/or misusing online data available on social sites. Thus, fake profile detection in social media networks is attracting the attention of researchers. This paper aims to discuss various machine learning (ML) methods used by researchers for fake profile detection to explore the further possibility of improvising the machine learning models for speedy results

The Interaction between Serotonin Transporter Allelic Variation and Maternal Care Modulates Instagram Sociability in a Sample of Singaporean Users

Human social interactions ensure recognition and approval from others, both in offline and online environments. This study applies a model from behavioral genetics on Instagram sociability to explore the impact of individual development on behavior on social networks. We hypothesize that sociable attitudes on Instagram resulted from an interaction between serotonin transporter gene alleles and the individual’s social relationship with caregivers. We assess the environmental and genetic components of 57 Instagram users. The self-report questionnaire Parental Bonding Instrument is adopted to determine the quality of parental bonding. The number of posts, followed users (“followings”), and followers are collected from Instagram as measures of online social activity. Additionally, the ratio between the number of followers and followings (“Social Desirability Index”) was calculated to estimate the asymmetry of each user’s social network. Finally, buccal mucosa cell samples were acquired, and the polymorphism rs25531 (T/T homozygotes vs. C-carriers) within the serotonin transporter gene was examined. In the preliminary analysis, we identified a gender effect on the number of followings. In addition, we specifically found a gene–environment interaction on the standardized Instagram “Social Desirability Index” in line with our predictions. Users with the genotype more sensitive to environmental influences (T/T homozygotes) showed a higher Instagram “Social Desirability Index” than nonsensitive ones (C-carriers) when they experienced positive maternal care. This result may contribute to understanding online social behavior from a gene*environment perspective

Unveiling Community Dynamics on Instagram Political Network

Online Social Networks (OSNs) allow users to generate and consume content in an easy and personalized way. Among OSNs, Instagram has seen a surge in popularity, and political actors exploit it to reach people at scale, bypassing traditional media and often triggering harsh debates with and among followers. Uncovering the structural properties and dynamics of such interactions is paramount for understanding the online political debate. This is a challenging task due to both the size of the network and the nature of interactions. In this paper, we define a probabilistic model to extract the backbone of the interaction network among Instagram commenters and, after that, we uncover communities. We apply our model to 10 weeks of comments centered around election times in Brazil and Italy. We monitor both politicians and other categories of influencers, finding persistent commenters, i.e., those who often comment together on Instagram posts. Our methodology allows us to unveil interesting facts: i) commenters’ networks are split into few communities; ii) community structure in politics is weaker than in general profiles, indicating that the political debate is a blur, with some commenters bridging strongly opposed political actors; and iii) communities engaging on political profiles are bigger, more active and more stable during electoral period

Investigation of IndexedDB Persistent Storage for Digital Forensics

The dependency on electronic services is increasing at a rapid rate in every aspect of our daily lives. While the Covid-19 virus remolded how we conduct business through remote collaboration applications, social media is rooting its grasp more in our day-in and day-out activities. Every day, a substantial amount of data is left in both desktop and web-based applications. As the size and the sophistication of stored data increases, so does the complexity of the technology that handles it. Consequently, forensic investigators are facing challenges in constantly adapting to emerging technologies. Hence, these technologies constitute the base for handling the vast size and volume of data in the modern era of information technology. In the scope of this dissertation the efficacy of emerging client-side technology, namely IndexedDB, is scrutinized for forensic value, practices of extraction, processing, presentation, and verification. Accordingly, a series of single case pretest-posttest quasi experiments are conducted to populate artifacts in the underlying storage technologies of IndexedDB. Subsequently, the populated artifacts are extracted and processed based on signature patterns and evaluated for their significance. Additionally, the artifacts are characterized, verified, and presented with the help of cornerstone tools that are implemented in this scope. Furthermore, time-frame analysis is constructed where it is possible to display ordered sequences of events for investigators in a suitable format

University Students’ Perspectives of Visual-based Cyberbullying on Instagram

Researchers have been investigating the cyberbullying phenomenon since the early 21st century. There is a substantial body of cyberbullying studies focused on text-based formats. However, studies revealed that visual-based social media platforms are more powerful than text-based platforms in affecting people’s emotions, causing significant psychological impact. Young adults ages 18-29 use visual-based social media heavily in their daily lives; therefore, visual cyberbullying on various sites has become a critical issue for this generation. Yet, the majority of existing cyberbullying studies focused on age groups under 18. The studies that did investigate this phenomenon among young adults focused mainly on text-based types of cyberbullying. Few studies have investigated visual-based cyberbullying of the adult population. Thus, this dissertation study explored university students’ perspectives of visual-based cyberbullying, with a specific focus on Instagram, because of its popularity. A Holistic Theoretical Framework was proposed to guide the study. This framework is grounded in the Social Ecological Model and the Cognitive-Affective-Behavioral frameworks. This study applied a mixed-method approach to collect data using four techniques: surveys, interviews, visual narrative inquiry, and scans of policy documents. Findings reported in this study have disclosed the nature of visual-based cyberbullying on Instagram as experienced by university students, revealed students’ perspectives of visual-based cyberbullying, unveiled the visual elements from actual incidents narrated by students, generated a novel definition of visual cyberbullying, and illuminated the gap between current university policies and real-world practices regarding the visual-based cyberbullying issue. This study contributes to the cyberbullying theoretical foundation, especially in exploring visual cyberbullying from cognitive, affective, and behavioral perspectives. Furthermore, the study collected visual cyberbullying cases that were crafted and narrated by study participants who witnessed cyberbullying incidents in real life. Future studies and practitioners may benefit from this study by applying the visual cases participants created to inform the design of research instruments and literacy educational materials. In addition, policymakers in higher education may learn from this study about the need to address cyberbullying more effectively in policy documents targeting undergraduate students. This study may also serve as a reference for the definition and examples of visual cyberbullying