My Tweets Bring All the Traits to the Yard: Predicting Personality and Relational Traits in Online Social Networks

Purpose– The purpose of this work is to analyse the relationships between the personality traits of linked users in online social networks. First the authors tried to discover relation patterns between personality dimensions in conversations. They also wanted to verify some hypotheses: whether users' personality is stable throughout different conversation threads and whether the similarity-attraction paradigm can be verified in this context. They used the five factor model of personality or Big Five, which has been widely studied in psychology.Design/methodology/approach– One of the approaches to detect users' personalities is by analysing the language they use when they talk to others. Based on this assumption the authors computed users' personality from the conversations extracted from the MySpace social network. Then the authors analysed the relationships among personality traits of users to discover patterns.Findings– The authors found that there are patterns between some personality dimensions in conversation threads, for example, agreeable people tend to communicate with extroverted people. They confirmed that the personality stability theory can be verified in social networks. Finally the authors could verify the similarity-attraction paradigm for some values of personality traits, such as extroversion, agreeableness, and openness to experience.Originality/value– The results the authors found provide some clues about how people communicate within online social networks, particularly who they tend to communicate with depending on their personality. The discovered patterns can be used in a wide range of applications, such as suggesting contacts in online social networks. Although some studies have been made regarding the role of personality in social media, no similar analysis has been done to evaluate how users communicate in social media considering their personality.

Trust among users in online social networks is a key factor in determining the amount of information that is perceived as reliable. Compared to the number of users in online social networks, user-specified trust relations are very sparse. This makes the pair-wise trust prediction a challenging task. Social studies have investigated trust and why people trust each other. The relation between trust and personality traits of people who established those relations, has been proved by social theories. In this work, we attempt to alleviate the effect of the sparsity of trust relations by extracting implicit information from the users, in particular, by focusing on users' personality traits and seeking a low-rank representation of users. We investigate the potential impact on the prediction of trust relations, by incorporating users' personality traits based on the Big Five factor personality model. We evaluate the impact of similarities of users' personality traits and the effect of each personality trait on pair-wise trust relations. Next, we formulate a new unsupervised trust prediction model based on tensor decomposition. Finally, we empirically evaluate this model using two real-world datasets. Our extensive experiments confirm the superior performance of our model compared to the state-of-the-art approaches.

In online social networking, people are getting involved in communications, but the real problem that comes to the mind is whether these communications can bring revolutions, and if so, who are the best people in given social networks that are really holding the remote control i.e. they have the power to bring these revolutions. This problem and the likes can be modelled as detecting influential users in online social networks. There can be many different ways to find influential users in social networks. Here we propose a novel method based upon tree data structure to determine the influential users in online social network. We identify influential nodes based on their positions in the network and assume that the nodes are homogeneous. This approach identifies the specific users who most influence others’ activity and does so considerably better than other methods. Cite this Article Hilal Ahmad Khanday, Rana Hashmy, Finding Influential Users in Online Social Networks: A Tree-based Approach. Journal of Artificial Intelligence Research & Advances . 2018; 5(3): 66–70p.

Personality traits and echo chambers on facebook

Many online social network (OSN) users exchange health-related information on OSNs to manage their health, obtain support, and offer assistance to others facing similar medical challenges. Nonetheless, OSN users are concerned about the privacy of their health information. Extant studies on online information privacy have largely resorted to a calculus perspective, focusing on the costs and benefits of information exchange with commercial vendors. Such a perspective, however, fails to capture the nuanced flows of personal health information (PHI) on OSNs, which involves more than two parties. To address this gap and further advance the research on information privacy, this paper embraces the theory of contextual integrity to explore the distinctive contextual idiosyncrasies of PHI flow on OSNs. In particular, we examine how context-specific values and norms affect OSN users’ personal privacy norms and their PHI disclosure intentions. Our theory-driven empirical results suggest that contextual values and normative expectations related to technology control, legislation, and peer users collectively influence OSN users’ personal privacy norms. In addition, perceived personal value and perceived altruistic value serve as motivators to promote PHI disclosure intentions, working in tandem with personal privacy norms. The key findings of the study also have important implications for policymakers and OSN providers in the context of OSN-mediated healthcare through (1) advancing our understanding of the complex dynamics of PHI flow on OSNs and (2) designing effective information privacy measures (i.e., policies, technical features, and programs) to uphold contextual integrity and nurture the development of patient-centered healthcare paradigms.

In social media, organizing friendship relationships is difficult since the more number of increasing users in Online Social Networks (OSN). To overcome this challenge, users in OSN heavily depend on grouping which is considered to be advantageous but at the same time found to be more cumbersome. More recently, recommender system and novel data clustering algorithm have been presented in OSN to address this concern. However, with the increasing nature and size of data (i.e., big data), grouping of users in OSN has become opened research for several academic’s professionals. In this paper, a novel clustering algorithm with optimized extreme machine learning which is called, Optimized Extreme Machine Learning and Orthogonally Projected (OEML-OP) is presented for user grouping in OSN to reducing computational overhead and time from large streams of social data. OEML-OP method utilizes new mechanism in EML to include optimality, a mechanism that is inspired by modularity function. The method performs user grouping through three main steps including, Duality Proportionality Graphical model, identifying optimal clusters and grouping of users in OSN. The Duality Proportionality Graphical model is to generate cluster of cliques and optimal clusters for the second steps and the Orthogonal Projection maximize the margin for separation between clusters. Due to the collective mechanism, the OEML-OP method gives better clustering accuracy and provides a novel model of grouping users on the basis of their activities. The analysis shows that the proposed method provides preferable clustering results and imparts a novel use-case of user grouping in OSN based on their activities.

Identifying influential users who lead to large-scale spreading in online social networks (OSNs) is of theoretical and practical significance. Many methods have been proposed to measure the influence of users, but little literature studies the interplay of the global influence of users and their local connections. In this paper, we particularly focus on the assortative and disassortative preference. The results in this paper can address three main issues in this area: (i) What is the difference in spreading influence between ordinary and core users? (ii) What is the distribution of influential users? (iii) How do they evolve from a fresh user to a powerful influencer? The k-shell hierarchy is adopted to quantify the global spreading influence of users. Firstly, we find that the global influence varies dramatically among users in disassortative OSNs, but the variation is relatively small in assortative OSNs. Hence, ordinary users also possess high influence in assortative OSNs. Secondly, we empirically and theoretically prove that the global influence of users follows a power-law distribution. Moreover, many users concentrate on the core in assortative OSNs, but few users locate at the core in disassortative OSNs. Thirdly, it is found that users in assortative OSNs gain influence over time and gradually upgrade to core members. In disassortative OSNs, the core users gain much influence along with network growth but other users scatter among all hierarchical levels. The results are verified on real OSN datasets and the state-of-the-art OSN model.

Finding influential users in online social networks is an important problem with many possible useful applications. HITS and other link analysis methods, in particular, have been often used to identify hub and authority users in web graphs and online social networks. These works, however, have not considered topical aspect of links in their analysis. A straightforward approach to overcome this limitation is to first apply topic models to learn the user topics before applying the HITS algorithm. In this paper, we instead propose a novel topic model known as Hub and Authority Topic (HAT) model to combine the two process so as to jointly learn the hub, authority and topical interests. We evaluate HAT against several existing state-of-the-art methods in two aspects: (i) modeling of topics, and (ii) link recommendation. We conduct experiments on two real-world datasets from Twitter and Instagram. Our experiment results show that HAT is comparable to state-of-the-art topic models in learning topics and it outperforms the state-of-the-art in link recommendation task.

Online social networks are now a popular way for users to connect, express themselves, and share content. Users in today's online social networks often post a profile, consisting of attributes like geographic location, interests, and schools attended. Such profile information is used on the sites as a basis for grouping users, for sharing content, and for suggesting users who may benefit from interaction. However, in practice, not all users provide these attributes.

PurposeThe purpose of this paper is to focus on college students, users of online social networks, as main sources of information that helps advertisers understand the ways in which advertisements are perceived online.Design/methodology/approachResults were reached through qualitative research. Personal in‐depth interviews, utilizing Zaltman Metaphor Elicitation Technique (ZMET), were conducted among 20 college students. Interviews consisted of using screenshots of advertisements in online social networks to uncover respondents' reactions.FindingsIt was generally concluded that the users of online social networks do not dislike advertisements, but they simply do not notice them. Other content found in online social networks mitigates the attractiveness of the advertisements. Hence, the respondents reported that the brand recognition in online social networks was found to be much lower than the one created through other media channels.Practical implicationsAdvertising in online social networks is a major unexplored advertising area. Interactivity on the internet shifts the ways in which users perceive advertising, and whether they perceive it at all. The paper discusses content that catches users' attention and its relation to advertisements.Originality/valueThrough literature review it has been revealed that no similar research exists. The findings of this research will aid advertisers in recognizing the possibility of advertising to the online social networks' population, taking into consideration different needs, and preferences of such users.

Many online social network (OSN) users are unaware of the numerous security risks that exist in these networks, including privacy violations, identity theft, and sexual harassment, just to name a few. According to recent studies, OSN users readily expose personal and private details about themselves, such as relationship status, date of birth, school name, email address, phone number, and even home address. This information, if put into the wrong hands, can be used to harm users both in the virtual world and in the real world. These risks become even more severe when the users are children. In this paper we present a thorough review of the different security and privacy risks which threaten the well-being of OSN users in general, and children in particular. In addition, we present an overview of existing solutions that can provide better protection, security, and privacy for OSN users. We also offer simple-to-implement recommendations for OSN users which can improve their security and privacy when using these platforms. Furthermore, we suggest future research directions.

Online social networking usage is growing rapidly, especially among at-risk populations, such as men who have sex with men (MSM). However, little research has studied the relationship between online social networking usage and sexual risk behaviors among at-risk populations. One hundred and eighteen Facebook-registered MSM (60.1% Latino, 28% African American; 11.9% other) were recruited from online (social networking websites and banner advertisements) and offline (local clinics, restaurants and organizations) venues frequented by minority MSM. Inclusion criteria required participants to be men who were 18 years of age or older, had had sex with a man in the past 12 months, were living in Los Angeles, and had a Facebook account. Participants completed an online survey on their social media usage and sexual risk behaviors. Results from a multivariable regression suggest that number of sexual partners met from online social networking technologies is associated with increased: 1) likelihood of having exchanged sex for food, drugs, or a place to stay within the past 3 months; 2) number of new partners within the past 3 months; 3) number of male sex partners within the past 3 months; and 4) frequency of engaging in oral sex within the past 3 months, controlling for age, race, education, and total number of sexual partners. Understanding the relationship between social media sex-seeking and sexual risk behaviors among at-risk populations will help inform population-focused HIV prevention and treatment interventions.

With a young population and high internet penetration levels, Saudi Arabian users are more active than ever on social media. Close to 44% of Saudi Arabia’s users of online social networking sites are between the age of 18 and 24 years, also known as ‘zoomers’ or ‘Gen Z’. From this age group, students at the college level are one of the most prolific users of online social networks ( Alaslani & Alandejani, 2020 ). Since this study was conducted on Saudi undergraduate students, we will refer to them as zoomers in this article as they are from the same age group. The Unified Theory of Acceptance and Use of Technology was adapted for the study to understand factors that influence the perception, acceptance and usage of online social networks by zoomers in Saudi Arabia. Data collected from 623 undergraduate students across the country reflect that while variables like task-oriented usage, trust and pleasure influenced the attitude of users, privacy was not a concern for students in choosing to use online social networks. Perceived behavioural control directly impacts the intention to use such networks; however, subjective norms or influence of peers and family did not impact the behavioural intention of the students to choose online social networking sites. A better understanding of the factors influencing the attitude of these zoomers (Gen Z) provides a massive opportunity for brands as well as social networking platforms to be able to not only reach this population effectively but also plan efficient approaches for revenue generation. This can also help academicians and government entities understand the behaviour of college-going students by exploring the constructs related to their behavioural intention to choose and use online social networks.

An enhanced trust prediction strategy for online social networks using probabilistic reputation features

ABSTRACTDue to the popularity and user friendliness of the Internet, numbers of users of online social networks (OSNs) and social media have grown significantly. However, globally utilised, social networks are the consequence of the lack of understanding of secrecy and protection on OSN and media has increased. Secrecy and surety of OSNs need to be inquired from various positions. According to recent studies, OSN users expose their private information such as email address, phone number etc. In this paper, we have presented a high-level classification of recent OSN attacks for recognising the problem and analysing the blow of such attacks on World Wide Web. We have also discussed OSN attacks on different social networking web applications by citing certain recent reports such as Kaspersky security network and Sophos security threat report. We also offer some simple-to-implement user practice tips to protect the system and user’s information. In addition to this, we have discussed a comprehensive analysis of numerous defensive approaches on OSN security. Lastly, based on the acknowledged strength and faults of these defensive approaches, we have explained open research issues.