Online Social Networking Services Research Articles

IRE: A Hypothesis or a Theory?

The paper addresses the issue of Russian English as a world variety used within such multicultural domain as Instagram which is an on-line social networking service. Building on an earlier idea, the paper puts forward a hypothesis regarding the existence of Instagram Russian English (IRE) which in general follows the pattern of Russian English in terms of its distinctive lexical, grammatical and pragmatic characteristics, the three-lectal continuum and the verbalization of the Russian mindset of its speakers. Therefore, IRE does reflect the Russian culture and mentality via both linguistic and pragmatic means.

Revisiting the connection between Solar eruptions and primary headaches and migraines using Twitter.

Many internal and environmental triggers of primary headaches have been proposed, but establishing firm evidence for any of them has proved elusive. Geomagnetic storms, the disturbances of Earth’s magnetic field following Solar eruptions, have been proposed as one such trigger. In this study, we utilized a vast amount of self-reported symptoms from the online social networking service Twitter in order to investigate a purported link between the level of geomagnetic activity and the onset of primary headaches and migraines. We analyzed 63 million keyword-bearing messages posted over the three years covering the maximum of Solar Cycle 24. No correlation has been found despite the large sample size. The simulation reveals that the significant correlation would have emerged even if only 1% of headache (2% of migraine) instances were caused by geomagnetic disturbances, thus placing very low upper limits on the prevalence of this trigger among Twitter users.

Estimation of structural properties of online social networks at the extreme

Sampling is a commonly used technique for studying structural properties of online social networks (OSNs). Due to privacy, business, and performance concerns, OSN service providers impose limitations on data access for third parties. The implication of this practice is that one needs to come up with an applicable sampling scheme that can function under these limitations to efficiently estimate structural properties of interest. In this paper, we study how accurately some important properties of graphs can be estimated under a limited data access model. More specifically, we consider random neighbor access (RNA) model as a rather limited data access model in OSNs. In the RNA model, the only query available to get data from the studied graph is the random neighbor query which returns the id of a random neighbor for a given vertex id. We propose various sampling schemes and estimators for average degree and network size under the RNA model. We conduct extensive experiments on both real world OSN graphs and synthetic graphs (1) to measure the performance of the proposed estimators and (2) to identify the factors affecting the accuracy of our estimators. We find that while the average degree estimators can make accurate estimations with reasonable sample sizes despite the extreme data access limitations of the RNA model, network size estimators require quite large sample sizes for accurate estimations.

Social-aware video delivery: challenges, approaches, and directions

We have recently witnessed the convergence of online social network services and online video services: users import videos from content sharing sites, and propagate them along the social connections by re-sharing them. Such social behaviors have dramatically reshaped how videos are disseminated, and users are now actively engaged as part of the social ecosystem, rather than being passive consumers. Despite the increasingly abundant bandwidth and computation resources, the ever increasing data volume of user generated video content and the boundless coverage of socialized sharing have presented unprecedented challenges. This article first presents the challenges in social-aware video delivery. Then we present a principal framework for social-aware video delivery and a state-of-the-art overview of approaches. Moreover, we identify the unique characteristics of social-aware video access and social content propagation, and reveal the design of individual modules and their integration toward enhancing users' experience in the social network context. Finally, we discuss future research directions.

Exploring the Effects of the Privacy‐Handling Management Styles of Social Networking Sites on User Satisfaction: A Conflict Management Perspective

ABSTRACTThe issue of consumer information privacy, arguably the most substantial and persistent problem confronting e‐commerce companies in general and online social networking service providers in particular, often results in unsatisfied customers. Researchers have investigated privacy from various perspectives and in a multitude of settings, yet there have been few attempts to understand privacy versus satisfaction, particularly from the perspective of conflict management. Because users’ privacy can be negatively affected by social networking sites (SNSs), this study focuses on the privacy conflicts between SNSs and their users. Drawing on conflict management theory, this article investigates the effects of different conflict management styles exhibited by an SNS on users’ satisfaction with its privacy practices, which then affects their intention to disclose personal information. The SNS examined in this study is Facebook, the most popular SNS with the largest number of users. Data were collected using an online survey and were analyzed using structural equation modeling. The results suggest that the two cooperative conflict management styles demonstrated by the SNS—accommodating and problem‐solving—positively affect users’ satisfaction with the privacy practices of the SNS both directly and indirectly through perceived privacy risk control. With regard to uncooperative styles, an avoiding style negatively affects user satisfaction directly, whereas an asserting style negatively affects user satisfaction indirectly through perceived privacy risk. The results also support that satisfaction is positively related to intention; users who are satisfied with the privacy practices of SNSs are more willing to disclose personal information than unsatisfied ones.

Improvement of recommender systems considering big data of users’ comments on chosen items

Regarding to the increase in the online social networks services during the recent years, the recommender system has turned into an emerging research subject. Currently, regarding to the fast and consistent expansion of using the internet, the necessity of a recommender system for refining the large volume of data has increased greatly. The purpose of recommender systems is to provide a list of the interested items for the user and due to the increase in the current data volume, the previous used tools are nor suitable for processing this data volume; hence, having a system which can save and process the large data has turned into a problem. In this study, to solve the mentioned problems, a system is recommended using a model-based collaborative filtering refinement model which uses the Spark processing model in Hadoop context in order for more exact advising the user from the viewpoint of the users. The obtained results indicate that the current method will be more efficient and effective compared to the common recommendation methods. Keywords : recommender systems; big data; sentiment analysis; hadoop; spark.

Improvement of recommender systems considering big data of users’ comments on chosen items

Regarding to the increase in the online social networks services during the recent years, the recommender system has turned into an emerging research subject. Currently, regarding to the fast and consistent expansion of using the internet, the necessity of a recommender system for refining the large volume of data has increased greatly. The purpose of recommender systems is to provide a list of the interested items for the user and due to the increase in the current data volume, the previous used tools are nor suitable for processing this data volume; hence, having a system which can save and process the large data has turned into a problem. In this study, to solve the mentioned problems, a system is recommended using a model-based collaborative filtering refinement model which uses the Spark processing model in Hadoop context in order for more exact advising the user from the viewpoint of the users. The obtained results indicate that the current method will be more efficient and effective compared to the common recommendation methods.Keywords: recommender systems; big data; sentiment analysis; hadoop; spark.

Research on China’s city network based on users’ friend relationships in online social networks: a case study of Sina Weibo

Cities no longer develop independently but exist in a large city network in the globalization era because of the rapid development of information and communication technologies. Instead of attribute data, relational data has been widely recognized as an improved tool to analyze the importance of a city in a city network. This study attempts to analyze China’s city network based on users’ friend relationships in Sina Weibo, the most popular online social networking service in China. By collecting friend relationship data from 20 users located in the selected 51 cities, a city network is built based on connections in the virtual world. Findings show that although microblog users build friend relationships with other users located almost everywhere, significant variations exist regarding the closeness of such connections. In particular, a limited number of cities accumulate the majority of top connections; the limited number indicates the significant power of these key cities in the city network. These top connections are also highly related to cities located in Eastern China. The regression model demonstrates that a city with a higher of per capita GDP exerts a powerful influence in the city network. Compared with a prefecture-level city, a municipality directly under central government is considerably powerful. Therefore, the importance of a city in the real world has significant influence on the power of that city in the city network based on online social networking connections.

Follow spam detection based on cascaded social information

In the last decade we have witnessed the explosive growth of online social networking services (SNSs) such as Facebook, Twitter, RenRen and LinkedIn. While SNSs provide diverse benefits for example, forstering inter-personal relationships, community formations and news propagation, they also attracted uninvited nuiance. Spammers abuse SNSs as vehicles to spread spams rapidly and widely. Spams, unsolicited or inappropriate messages, significantly impair the credibility and reliability of services. Therefore, detecting spammers has become an urgent and critical issue in SNSs. This paper deals with Follow spam in Twitter. Instead of spreading annoying messages to the public, a spammer follows (subscribes to) legitimate users, and followed a legitimate user. Based on the assumption that the online relationships of spammers are different from those of legitimate users, we proposed classification schemes that detect follow spammers. Particularly, we focused on cascaded social relations and devised two schemes, TSP-Filtering and SS-Filtering, each of which utilizes Triad Significance Profile (TSP) and Social status (SS) in a two-hop subnetwork centered at each other. We also propose an emsemble technique, Cascaded-Filtering, that combine both TSP and SS properties. Our experiments on real Twitter datasets demonstrated that the proposed three approaches are very practical. The proposed schemes are scalable because instead of analyzing the whole network, they inspect user-centered two hop social networks. Our performance study showed that proposed methods yield significantly better performance than prior scheme in terms of true positives and false positives.

Relationship Identification Across Heterogeneous Online Social Networks

In the era of the social web, many people manage their social relationships through various online social networking services. It has been found that identifying the types of social relationships among users in online social networks facilitates the marketing of products via electronic “word of mouth.” However, it is a great challenge to identify the types of social relationships, given very limited information in a social network. In this article, we study how to identify the types of relationships across multiple heterogeneous social networks and examine if combining certain information from different social networks can help improve the identification accuracy. The main contribution of our research is that we develop a novel decision tree initiated random walk model, which takes into account both global network structure and local user behavior to bootstrap the performance of relationship identification. Experiments conducted based on two real‐world social networks, Sina Weibo and Jiepang, demonstrate that the proposed model achieves an average accuracy of 92.0%, significantly outperforming other baseline methods. Our experiments also confirm the effectiveness of combining information from multiple social networks. Moreover, our results reveal that human mobility features indicating location categories, coincidence, and check‐in patterns are among the most discriminative features for relationship identification.

A regulatory model for personal data on social networking services in the UK

Widespread use of online social networking services (SNSs) exposes users to a variety of risks. This study examines the UK’s Data Protection Act 1998 (DPA) and considers the wider regulatory landscape in the UK. Although based on EU legislation, the DPA has shortcomings in enforcement and in regulating global services using national legislation. Lessig’s model of internet regulation was used as a starting point to examine the alternative regulatory mechanisms that apply to personal data on SNSs. Interviews with industry experts highlighted self-regulation as a major influence on the behaviour of users and SNS providers. This has been incorporated into a new model of regulation that applies to SNSs. The resulting model has identified the following modes: law (statutory legislation), self-regulation (privacy policies and self-regulation of the online advertising industry), code (the way services are designed and their system architecture), and norms (expressed as user behaviour and collectively as market behaviour). The paper concludes that this new model of regulation is needed to adequately describe the current regulatory landscape as it applies to social media. This may form a better basis for evaluation of regulatory effectiveness in the future.

P211 BASHH on Twitter: social media for sexual health: Abstract P211 Table 1

Background/introduction Twitter is a free, online social networking service through which users can read and send brief messages of up to 140-characters called “Tweets.” BASHH (@BASHH_UK) currently has 3479 followers on Twitter. These include professionals working in sexual health, sexual health services with their own Twitter accounts and members of the public. Twitter disseminates messages instantly and can influence a wide audience. It can be used to keep health professionals and patients informed about relevant research and headline news. For example, recent Twitter themes have included discussions about PrEP and gonorrhoea resistance. Twitter is also used for sharing practical information such as conference deadlines. Aim(s)/objectives To evaluate the reach of BASHH on Twitter by reporting BASHH’s Twitter activity. Methods Using Twitter Analytics, data was obtained on the number of Tweets and Tweet Impressions for each of the last 4 months. Tweet Impressions are counted as the number of times each Tweet is seen. The number of visits to BASHH’s Twitter profile, mentions by other Twitter users and number of new followers are also recorded. This is presented below: Results Discussion/conclusion BASHH’s Twitter activity is reaching a large audience and increasing. All individuals working in sexual health and HIV should consider using Twitter to support wider engagement with their professional network.

Inference Attack on Browsing History of Twitter Users Using Public Click Analytics and Twitter Metadata

Twitter is a popular online social network service for sharing short messages (tweets) among friends. Its users frequently use URL shortening services that provide (i) a short alias of a long URL for sharing it via tweets and (ii) public click analytics of shortened URLs. The public click analytics is provided in an aggregated form to preserve the privacy of individual users. In this paper, we propose practical attack techniques inferring who clicks which shortened URLs on Twitter using the combination of public information: Twitter metadata and public click analytics. Unlike the conventional browser history stealing attacks, our attacks only demand publicly available information provided by Twitter and URL shortening services. Evaluation results show that our attack can compromise Twitter users’ privacy with high accuracy.

The online behavior of pupils with visual impairment: A preliminary report

BackgroundYoung people with special educational needs are also users of new media. ObjectiveThe aim of the study was to determine whether pupils with visual impairment spend more time on Internet use than pupils without visual impairment and whether youngsters from the study group are engaged in behaviors which could increase risk of problematic Internet use. MethodsThe study is based on a total of 191 pupils: a study group of 66 partially-sighted pupils and a control group comprised 125 pupils who do not have visual impairment. A self-report questionnaire was constructed for the needs of this study. ResultsPupils with visual impairment spent significantly less time on Internet use than their peers from the reference groups. Twice as many pupils from the study group as controls reported no symptoms suggesting potentially problematic Internet use. In addition, the study group more frequently used online social networking services and e-mails or used the Internet for assignments. ConclusionsLittle difference exists between students with visual impairment and those without with regard to Internet use.

Measuring user influence on Twitter: A survey

Centrality is one of the most studied concepts in social network analysis. There is a huge literature regarding centrality measures, as ways to identify the most relevant users in a social network. The challenge is to find measures that can be computed efficiently, and that can be able to classify the users according to relevance criteria as close as possible to reality. We address this problem in the context of the Twitter network, an online social networking service with millions of users and an impressive flow of messages that are published and spread daily by interactions between users. Twitter has different types of users, but the greatest utility lies in finding the most influential ones. The purpose of this article is to collect and classify the different Twitter influence measures that exist so far in literature. These measures are very diverse. Some are based on simple metrics provided by the Twitter API, while others are based on complex mathematical models. Several measures are based on the PageRank algorithm, traditionally used to rank the websites on the Internet. Some others consider the timeline of publication, others the content of the messages, some are focused on specific topics, and others try to make predictions. We consider all these aspects, and some additional ones. Furthermore, we include measures of activity and popularity, the traditional mechanisms to correlate measures, and some important aspects of computational complexity for this particular context.

Information quality in healthcare social media – an architectural approach

The evolution of the Internet has provided new and more efficient communication and collaboration channels between individuals, empowering them to share health information and to interact more and more using social media channels. Information quality is frequently identified as one of the main limitations of social media in healthcare. The objective of the paper is to discuss the social implications and limitations on information quality in healthcare social media, and particularly to contribute to the analysis and development of healthcare social networks (SN) and online healthcare social network services (SNS). For that reason, a formal architectural analysis of healthcare SN and SNS has been proposed, considering the complexity of both systems, but stressing on quality of Information aspects. Any social system - and its network of interrelations - can be analyzed as a real world system. Therefore, it is possible to model the structural components, interrelations and functionality (system architecture) of healthcare SN and SNS. The Generic Component Model (GCM) has demonstrated to be an ideal architectural framework for any real system’s analysis. Based on the GCM, the architecture of quality aware healthcare SNS is proposed. Information quality is addressed in the domain dimension of the GCM, supported by the definition of information quality policies. The practical implementation of quality-driven online SNS and information quality policies was demonstrated by the development of an online SNS supporting health promotion programs in a healthcare institution. In order to evaluate the relevance of the information quality policies proposed, a descriptive study was performed, in which the content published in the online SNS was selected strictly following the information quality policy. The study demonstrated that users’ perceived information quality was high. The paper addresses sociotechnical aspects of social media by providing a formal method for the management of information quality in online healthcare social networks.

Proactive Cache Placement on Cooperative Client Caches for Online Social Networks

This paper investigates cache placement on a cooperative cache built from individual client caches in an online social network or web service. We use a service that maintains a mapping between content and the clients that cache it, and propose cache placement schemes that leverage relationships between clients (for example, social links) and workload statistics, proactively placing content on clients that are likely to access it. We evaluate efficacy through simulation, comparing our schemes against commonly used cache placement algorithms as well as optimal placement. We synthesize a workload to match characteristics of online social networks. Simulation results of our proposed caching schemes impose moderate network overhead and show considerable improvement to the client's cache hit ratio, even under churn.

Dynamic community detection in evolving networks using locality modularity optimization

The amount and the variety of data generated by today’s online social and telecommunication network services are changing the way researchers analyze social networks. Facing fast evolving networks with millions of nodes and edges are, among other factors, its main challenge. Community detection algorithms in these conditions have also to be updated or improved. Previous state-of-the-art algorithms based on the modularity optimization (i.e. Louvain algorithm), provide fast, efficient and robust community detection on large static networks. Nonetheless, due to the high computing complexity of these algorithms, the use of batch techniques in dynamic networks requires to perform network community detection for the whole network in each one of the evolution steps. This fact reveals to be computationally expensive and unstable in terms of tracking of communities. Our contribution is a novel technique that maintains the community structure always up-to-date following the addition or removal of nodes and edges. The proposed algorithm performs a local modularity optimization that maximizes the modularity gain function only for those communities where the editing of nodes and edges was performed, keeping the rest of the network unchanged. The effectiveness of our algorithm is demonstrated with the comparison to other state-of-the-art community detection algorithms with respect to Newman’s Modularity, Modularity with Split Penalty, Modularity Density, number of detected communities and running time.

Dispersing Instant Social Video Service Across Multiple Clouds

Instant social video sharing which combines the online social network and user-generated short video streaming services, has become popular in today's Internet. Cloud-based hosting of such instant social video contents has become a norm to serve the increasing users with user-generated contents. A fundamental problem of cloud-based social video sharing service is that users are located globally, who cannot be served with good service quality with a single cloud provider. In this paper, we investigate the feasibility of dispersing instant social video contents to multiple cloud providers. The challenge is that inter-cloud social propagation is indispensable with such multi-cloud social video hosting, yet such inter-cloud traffic incurs substantial operational cost. We analyze and formulate the multi-cloud hosting of an instant social video system as an optimization problem. We conduct large-scale measurement studies to show the characteristics of instant social video deployment, and demonstrate the trade-off between satisfying users with their ideal cloud providers, and reducing the inter-cloud data propagation. Our measurement insights of the social propagation allow us to propose a heuristic algorithm with acceptable complexity to solve the optimization problem, by partitioning a propagation-weighted social graph in two phases: a preference-aware initial cloud provider selection and a propagation-aware re-hosting. Our simulation experiments driven by real-world social network traces show the superiority of our design.

Abstract A13: African American breast cancer survivors in the digital age

Abstract The information needs of breast cancer survivors are varied and complex. How African American breast cancer survivors (AABCS) access cancer-related information and the extent to which they find the information helpful is not well understood. We conducted a statewide survey with 297 female AABCS and led 8 in-depth focus groups with 60 female AABCS to understand where AABCS look for health information and their preferred format of materials. We were also interested in the extent to which AABCS used social media services to look for information or to bolster their supportive networks. The average age of those who completed the survey was 57.5 years, and almost all (95%) received surgical treatment for their breast cancer; 77% received radiotherapy and 53% chemotherapy. Almost all (95%) reported at least one co-morbid health condition such as hypertension or diabetes; 22% reported five or more co-morbid conditions. The majority of AABCS (85%) reported using informational books or brochures, followed by in-person talks or lectures by experts (56%). AABCS also found these two formats to be the most helpful. Not surprisingly, most AABCS preferred to receive information in written form (59%) or in-person (60%). Although most of the AABCS in our sample (70%) had access to the Internet in their home and many (61%) were on Facebook, the majority (63%) reported that they are not members of social networking sites such as Twitter or Google Plus, and few (<10%) reported using these platforms for sharing their cancer-related experiences, connecting to other survivors, or accessing health-related information. The discussions of AABCSs in the focus groups echo these findings with stories of difficulty finding information tailored to their needs and frustration that too many resources are only available online. The results from this mixed-methods study suggest that while many organizations have moved their cancer information online, there are segments of the population, such as AABCS, who continue to rely on and prefer print materials and in-person formats to manage their health care needs. Organizations should aim to increase AABCS' familiarity and ease of accessing online health information, as many AABCS have access to the Internet yet do not perceive online materials or social networking services to be as helpful for addressing their needs. Citation Format: Amy Leader, Melissa DiCarlo, Patricia Bradley, Sarah Hagerty, Andrea Barsevick. African American breast cancer survivors in the digital age. [abstract]. In: Proceedings of the Eighth AACR Conference on The Science of Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; Nov 13-16, 2015; Atlanta, GA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2016;25(3 Suppl):Abstract nr A13.