A qualitative study of stakeholders' perspectives on the social network service environment

Over two billion people are using the Internet at present, assisted by the mediating activities of software agents which deal with the diversity and complexity of information. There are, however, ethical issues due to the monitoring-and-surveillance, data mining and autonomous nature of software agents. Considering the context, this study aims to comprehend stakeholders' perspectives on the social network service environment in order to identify the main considerations for the design of software agents in social network services in the near future. Twenty-one stakeholders, belonging to three key stakeholder groups, were recruited using a purposive sampling strategy for unstandardised semi-structured e-mail interviews. The interview data were analysed using a qualitative content analysis method. It was possible to identify three main considerations for the design of software agents in social network services, which were classified into the following categories: comprehensive understanding of users' perception of privacy, user type recognition algorithms for software agent development and existing software agents enhancement

Predicting individuals' vulnerability to social engineering in social networks

The popularity of social networking sites has attracted billions of users to engage and share their information on these networks. The vast amount of circulating data and information expose these networks to several security risks. Social engineering is one of the most common types of threat that may face social network users. Training and increasing users’ awareness of such threats is essential for maintaining continuous and safe use of social networking services. Identifying the most vulnerable users in order to target them for these training programs is desirable for increasing the effectiveness of such programs. Few studies have investigated the effect of individuals’ characteristics on predicting their vulnerability to social engineering in the context of social networks. To address this gap, the present study developed a novel model to predict user vulnerability based on several perspectives of user characteristics. The proposed model includes interactions between different social network-oriented factors such as level of involvement in the network, motivation to use the network, and competence in dealing with threats on the network. The results of this research indicate that most of the considered user characteristics are factors that influence user vulnerability either directly or indirectly. Furthermore, the present study provides evidence that individuals’ characteristics can identify vulnerable users so that these risks can be considered when designing training and awareness programs

Categorizing Young Facebook Users Based On Their Differential Preference of Social Media Heuristics: A Q-Methodology Approach

Background: Social media have become an integral part of our modern society by providing platforms for users to create and exchange news, ideas, and information. The increasing use of social media has raised concerns about the reliability of the shared information, particularly information that is generated from anonymous users. Though prior studies have confirmed the important roles of heuristics and cues in the users’ evaluation of trustworthy information, there has been no research–to our knowledge–that categorized Facebook users based on their approaches to evaluating information credibility. Method: We employed Q-methodology to extract insights from 55 young Vietnamese users and to categorize them into different groups based on the distinct sets of heuristics that they used to evaluate the trustworthiness of online information on Facebook. Results: We identified four distinct types of young Facebook user groups that emerged based on their evaluation of online information trustworthiness. When evaluating online information trustworthiness on Facebook, these user groups assigned priorities differently to the characteristics of the online content, its original source, and the sharers or aggregators. We named these groups: (1) the balanced analyst, (2) the critical analyst, (3) the source analyst, and (4) the social network analyst. Conclusion: The findings offer insights that contribute to information processing literature. Moreover, marketing practitioners who aim to disseminate information effectively on social networks should take these user groups’ perspectives into consideration

VNE solution for network differentiated QoS and security requirements: from the perspective of deep reinforcement learning

The rapid development and deployment of network services has brought a series of challenges to researchers. On the one hand, the needs of Internet end users/applications reflect the characteristics of travel alienation, and they pursue different perspectives of service quality. On the other hand, with the explosive growth of information in the era of big data, a lot of private information is stored in the network. End users/applications naturally start to pay attention to network security. In order to solve the requirements of differentiated quality of service (QoS) and security, this paper proposes a virtual network embedding (VNE) algorithm based on deep reinforcement learning (DRL), aiming at the CPU, bandwidth, delay and security attributes of substrate network. DRL agent is trained in the network environment constructed by the above attributes. The purpose is to deduce the mapping probability of each substrate node and map the virtual node according to this probability. Finally, the breadth first strategy (BFS) is used to map the virtual links. In the experimental stage, the algorithm based on DRL is compared with other representative algorithms in three aspects: long term average revenue, long term revenue consumption ratio and acceptance rate. The results show that the algorithm proposed in this paper has achieved good experimental results, which proves that the algorithm can be effectively applied to solve the end user/application differentiated QoS and security requirements

BotMoE: Twitter Bot Detection with Community-Aware Mixtures of Modal-Specific Experts

Twitter bot detection has become a crucial task in efforts to combat online misinformation, mitigate election interference, and curb malicious propaganda. However, advanced Twitter bots often attempt to mimic the characteristics of genuine users through feature manipulation and disguise themselves to fit in diverse user communities, posing challenges for existing Twitter bot detection models. To this end, we propose BotMoE, a Twitter bot detection framework that jointly utilizes multiple user information modalities (metadata, textual content, network structure) to improve the detection of deceptive bots. Furthermore, BotMoE incorporates a community-aware Mixture-of-Experts (MoE) layer to improve domain generalization and adapt to different Twitter communities. Specifically, BotMoE constructs modal-specific encoders for metadata features, textual content, and graphical structure, which jointly model Twitter users from three modal-specific perspectives. We then employ a community-aware MoE layer to automatically assign users to different communities and leverage the corresponding expert networks. Finally, user representations from metadata, text, and graph perspectives are fused with an expert fusion layer, combining all three modalities while measuring the consistency of user information. Extensive experiments demonstrate that BotMoE significantly advances the state-of-the-art on three Twitter bot detection benchmarks. Studies also confirm that BotMoE captures advanced and evasive bots, alleviates the reliance on training data, and better generalizes to new and previously unseen user communities.Comment: Accepted at SIGIR 202

Trail Users in the Cincinnati Metropolitan Region: Purposes, Patterns, and Preferences

The benefits of using multi-use trails have been recognized from different perspectives, such as improving public health, expanding active transportation options, and enhancing environmental quality. Trail managers in Greater Cincinnati have developed a 212-mile trail network, with plans to expand and connect the system. Given regional priorities for trail development, trail managers and advocates need to understand more about trail users and how they use the network. In response, two nonprofit organizations in this region, Tri-State Trails and Interact for Health, along with the assistance from researchers at the University of Minnesota, launched Greater Cincinnati’s first comprehensive trail measurement program including both trail traffic monitoring and an intercept survey of trail users. Monitoring results show the network is heavily used: in 2017, monitoring results on 137 miles of the network showed users traveled an estimated 11 million miles on those segments annually (Lindsey et al. 2019). This paper describes results of the survey which was designed with questions covering trip characteristics, perceptions of the trails, socio-demographics, and locational information. Between August 2017 and October 2017, 31 trail staff and volunteers administered the survey at 20 locations. 734 responses were obtained. Three methods are used to analyze the survey: descriptive summary, statistical association analysis, and geographical mapping. The descriptive results show 89% of respondents are recreational users while only 8.8% are utilitarian users. These utilitarian users cluster in areas close to Cincinnati downtown and along a centrally located, long trail that connects several communities. Most recreational users are female, white, between 35 and 64 years old, well-educated, and with relatively high incomes. These users primarily bicycled and walked on trails, drove less than 25 minutes to trail, and traveled less than 5 miles. In contrast, most utilitarian users are male, with income of less than $59,999, walked or biked to trail, and traveled no more than 2 miles on trails. Recreational and utilitarian users’ preferences are consistent with positive attitudes towards trail use and environment. Some differences between recreational and utilitarian users are statistically supported using the statistical association analysis. Geographically, the neighborhood context of trail users with different socio-demographics is displayed to illustrate clustering phenomenon among trail users by race and income. The differences between recreational and utilitarian users imply the importance of developing policies to satisfy various needs of trail users. This analysis provides a valuable framework for local governments to evaluate, manage, and improve the multi-use trail network

The synergistic and dynamic relationship between learning design and learning analytics

The synergistic relationship between learning design and learning analytics has the potential for improving learning and teaching in near real-time. The potential for integrating the newly available and dynamic information from ongoing analysis into learning design requires new perspectives on learning and teaching data processing and analysis as well as advanced theories, methods, and tools for supporting dynamic learning design processes. Three perspectives of learning analytics design provide summative, real-time, and predictive insights. In a case study with 3,550 users, the navigation sequence and network graph analysis demonstrate the potential of learning analytics design. The study aims to demonstrate how the analysis of navigation patterns and network graph analysis could inform the learning design of self-guided digital learning experiences. Even with open-ended freedom, only 608 sequences were evidenced by learners out of a potential number of hundreds of millions of sequences. Advancements of learning analytics design have the potential for mapping the cognitive, social and even physical states of the learner and optimise their learning environment on the fly

Modeling Twitter Engagement in Real-World Events

Twitter offers tremendous opportunities for people to engage with real-world events (e.g., political election) through information sharing and communicating about these events. However, little is understood about the factors that affect people’s Twitter engagement (e.g., posting) in such real-world events. This paper examines multiple predictive factors associated with four different perspectives of users’ Twitter engagement, and quantify their potential influence on predicting the (i) presence; and (ii) degree of the user’s engagement with real-world events. We find that the measures of people’s prior Twitter activities, topical interests, geolocation, and social network structures are all variously correlated to their engagement with real-world events.