Suchergebnisse

The impact of social networks in customer buying decisions is rapidly increasing, because they are effective in shaping public opinion. This paper helps marketers analyze a social network's members based on different characteristics as well as choose the best method for identifying influential people among them. Marketers can then use these influential people as seeds for market products/services. Considering the importance of opinion leadership in social networks, the authors provide a comprehensive overview of existing literature. Studies show that different titles (such as opinion leaders, influential people, market mavens, and key players) are used to refer to the influential group in social networks. In this paper, all the properties presented for opinion leaders in the form of different titles are classified into three general categories, including structural, relational, and personal characteristics. Furthermore, based on studying opinion leader identification methods, appropriate parameters are extracted in a comprehensive chart to evaluate and compare these methods accurately.

The current study investigates engagement activities in an online resource exchange community exploring elements such as sense of belonging, connectedness, and trust. CouchSurfing.com is an online cultural exchange community in which members from around the globe coordinate travel accommodations and organize gatherings with fellow members via a social media platform. Findings confirmed that members who have not met face-to-face with other members have a lower sense of belonging to the community than those who have. Increased attendance to gatherings was positively related to sense of belonging to the community, and hosting had a positive relationship with trust in the community. Additionally, CouchSurfers reported that they preferred to be contacted through personal e-mails rather then group e-mails, while those who reported an increased participation in gatherings found group e-mails to be useful.

This paper suggests a methodological improvement to study social capital in online social networks. We have designed a measurement tool based on Lin's theory of social resources. It is named Social Village and can be accessed in (http://socialvillage.me). By this tool, we are getting access to profile and friendship data of users of online social networks (Facebook and Google Plus). To access this data, we ask for users' permission by social login and we have designed a gamified and interesting social survey that helps users get an in-depth knowledge of their online life. This tool combines three structural generators for social capital data (name, position and resource generators) and it has been developed in three languages (English, French and Persian) enabling us to conduct comparative studies. Based on our preliminary results presented in this paper, 412 users in sample of our study know who they are connected with in online social networks, they know their friends' socio-economic positions and they are providing or receiving various resources through their online friendships. Gamified social survey used in this tool helped us gain a four times more response rate than existing online surveys. In this paper, we present, reviewed literature, theoretical framework, methodology of constructing the tool and results obtained.

"The final publication is available at Springer via http://dx.doi.org/10.1007/s10588-013-9156-z ; We present a new mathematical model that predicts the number of users informed and influenced by messages that are propagated in an online social network. Our model is based on a new way of quantifying the tie-strength, which in turn considers the affinity and relevance between nodes. We could verify that the messages to inform and influence, as well as their importance, produce different propagation behaviors in an online social network. We carried out laboratory tests with our model and with the baseline models Linear Threshold and Independent Cascade, which are currently used in many scientific works. The results were evaluated by comparing them with empirical data. The tests show conclusively that the predictions of our model are notably more accurate and precise than the predictions of the baseline models. Our model can contribute to the development of models that maximize the propagation of messages; to predict the spread of viruses in computer networks, mobile telephony and online social networks. ; This work was carried out by the financing of the Ecuadorian government of President Rafael Correa D. and by FLAMINGO, a Network of Excellence project (318488) supported by the European Commission under the Seventh Framework Program and the project TEC2015-71329-C2-2-R (MINECO/FEDER) from Ministerio de Economía y Competitividad. R.M.O-G. thanks Dr. Joan Serrat Fernández, Dr. Xavier Muñoz and Dr. Josep Fàbrega, professors at the University Polytechnic of Catalonia. Similarly, Dr. Esteban Samaniego Alvarado and PhD student Vladimiro Tobar Solano, professors at Universidad de Cuenca, and the PhD student Lucía Mendez Tapia for the accomplishment of this work. ; Peer Reviewed ; Postprint (author's final draft)

International audience ; Facebook News Feed personalization algorithm has a significant impact, on a daily basis, on the lifestyle, mood and opinion of millions of Internet users. Nonetheless, the behavior of such algorithm lacks transparency, motivating measurements, modeling and analysis in order to understand and improve its properties. In this paper, we propose a reproducible methodology encompassing measurements, an analytical model and a fairness-based News Feed design. The model leverages the versatility and analytical tractability of time-to-live (TTL) counters to capture the visibility and occupancy of publishers over a News Feed. Measurements are used to parameterize and to validate the expressive power of the proposed model. Then, we conduct a what-if analysis to assess the visibility and occupancy bias incurred by users against a baseline derived from the model. Our results indicate that a significant bias exists and it is more prominent at the top position of the News Feed. In addition, we find that the bias is non-negligible even for users that are deliberately set as neutral with respect to their political views, motivating the proposal of a novel and more transparent fairness-based News Feed design.

International audience ; Facebook News Feed personalization algorithm has a significant impact, on a daily basis, on the lifestyle, mood and opinion of millions of Internet users. Nonetheless, the behavior of such algorithm lacks transparency, motivating measurements, modeling and analysis in order to understand and improve its properties. In this paper, we propose a reproducible methodology encompassing measurements, an analytical model and a fairness-based News Feed design. The model leverages the versatility and analytical tractability of time-to-live (TTL) counters to capture the visibility and occupancy of publishers over a News Feed. Measurements are used to parameterize and to validate the expressive power of the proposed model. Then, we conduct a what-if analysis to assess the visibility and occupancy bias incurred by users against a baseline derived from the model. Our results indicate that a significant bias exists and it is more prominent at the top position of the News Feed. In addition, we find that the bias is non-negligible even for users that are deliberately set as neutral with respect to their political views, motivating the proposal of a novel and more transparent fairness-based News Feed design.

International audience ; Facebook News Feed personalization algorithm has a significant impact, on a daily basis, on the lifestyle, mood and opinion of millions of Internet users. Nonetheless, the behavior of such algorithm lacks transparency, motivating measurements, modeling and analysis in order to understand and improve its properties. In this paper, we propose a reproducible methodology encompassing measurements, an analytical model and a fairness-based News Feed design. The model leverages the versatility and analytical tractability of time-to-live (TTL) counters to capture the visibility and occupancy of publishers over a News Feed. Measurements are used to parameterize and to validate the expressive power of the proposed model. Then, we conduct a what-if analysis to assess the visibility and occupancy bias incurred by users against a baseline derived from the model. Our results indicate that a significant bias exists and it is more prominent at the top position of the News Feed. In addition, we find that the bias is non-negligible even for users that are deliberately set as neutral with respect to their political views, motivating the proposal of a novel and more transparent fairness-based News Feed design.

International audience ; Facebook News Feed personalization algorithm has a significant impact, on a daily basis, on the lifestyle, mood and opinion of millions of Internet users. Nonetheless, the behavior of such algorithm lacks transparency, motivating measurements, modeling and analysis in order to understand and improve its properties. In this paper, we propose a reproducible methodology encompassing measurements, an analytical model and a fairness-based News Feed design. The model leverages the versatility and analytical tractability of time-to-live (TTL) counters to capture the visibility and occupancy of publishers over a News Feed. Measurements are used to parameterize and to validate the expressive power of the proposed model. Then, we conduct a what-if analysis to assess the visibility and occupancy bias incurred by users against a baseline derived from the model. Our results indicate that a significant bias exists and it is more prominent at the top position of the News Feed. In addition, we find that the bias is non-negligible even for users that are deliberately set as neutral with respect to their political views, motivating the proposal of a novel and more transparent fairness-based News Feed design.

International audience ; Facebook News Feed personalization algorithm has a significant impact, on a daily basis, on the lifestyle, mood and opinion of millions of Internet users. Nonetheless, the behavior of such algorithm lacks transparency, motivating measurements, modeling and analysis in order to understand and improve its properties. In this paper, we propose a reproducible methodology encompassing measurements, an analytical model and a fairness-based News Feed design. The model leverages the versatility and analytical tractability of time-to-live (TTL) counters to capture the visibility and occupancy of publishers over a News Feed. Measurements are used to parameterize and to validate the expressive power of the proposed model. Then, we conduct a what-if analysis to assess the visibility and occupancy bias incurred by users against a baseline derived from the model. Our results indicate that a significant bias exists and it is more prominent at the top position of the News Feed. In addition, we find that the bias is non-negligible even for users that are deliberately set as neutral with respect to their political views, motivating the proposal of a novel and more transparent fairness-based News Feed design.

Purpose
This study aims to determine the effect of online social networks on university students' environmentally responsible behavior (ERB). This research aimed to develop and test a behavioral model in the context of online social networks, where students' attitudes, knowledge and behavior influence their ERB.


Design/methodology/approach
This study used a quasi-experiment with a pretest-posttest design and a random parallelization control group. The research used a questionnaire to assess ERB, environmental attitudes and environmental knowledge. The researcher randomly assigned 120 students to an experimental and a control group of equal size. Both groups initially completed a pretest. The experimental group was trained in environmental issues over four months (an academic semester) via an online social network.


Findings
The findings indicated that the social network had a significant effect on motivating ERB. Additionally, it improved environmental attitudes. According to the results, online social networks such as Facebook can significantly aid in teaching and learning environmental issues in formal academic settings.


Originality/value
Online social networks facilitated significant cognitive progress in environmental education. The primary objective is to educate students about ERB.

Billions of users interact intensively every day via Online Social Networks (OSNs) such as Facebook, Twitter, or Google+. This makes OSNs an invaluable source of information, and channel of actuation, for sectors like advertising, marketing, or politics. To get the most of OSNs, analysts need to identify influential users that can be leveraged for promoting products, distributing messages, or improving the image of companies. In this report we propose a new unsupervised method, Massive Unsupervised Outlier Detection (MUOD), based on outliers detection, for providing support in the identification of influential users. MUOD is scalable, and can hence be used in large OSNs. Moreover, it labels the outliers as of shape, magnitude, or amplitude, depending of their features. This allows classifying the outlier users in multiple different classes, which are likely to include different types of influential users. Applying MUOD to a subset of roughly 400 million Google+ users, it has allowed identifying and discriminating automatically sets of outlier users, which present features associated to different definitions of influential users, like capacity to attract engagement, capacity to attract a large number of followers, or high infection capacity. ; Partially supported by Ministerio de Economia y Competitividad grant ECO2015-66593-P, Regional Government of Madrid (CM) grant Cloud4BigData (S2013/ICE-2894, co-funded by FSE & FEDER), the European Union through the ReCRED (653417) project and MIT MISTI Global Seed Funds through the MyBubble project.

Mapping and assessing social networks and the quality of their social support is a valuable intervention strategy for social workers. These networks have now spread onto the digital realm in the form of Online Social Networks (OSNs). This study investigated the nature of social support provided by such networks to their users in a rural mid-South University (USA) and explored parallels with the current understanding of social support in conventional social networks. A web-based survey administered to college students revealed that users of these online networks were predominantly undergraduate first year students, female, single, unemployed and from a variety of academic disciplines. The examination of the components of OSNs appears to mirror those of offline networks. They also seem to complement the effects of each other while contributing to an individual's support system. The paper concludes with critical implications of such online social networking for University students and social workers in practice and education.

Online social networks are designed to encourage disclosure while also having the ability to disrupt existing privacy boundaries. This study assesses those individuals who are the most active online: "Digital Natives." The specific focus includes participants' privacy beliefs; how valuable they believe their personal, private information to be; and what risks they perceive in terms of disclosing this information in a fairly anonymous online setting. A model incorporating these concepts was tested in the context of communication privacy management theory. Study findings suggest that attitudinal measures were stronger predictors of privacy behaviors than were social locators. In particular, support was found for a model positing that if an individual placed a higher premium on their personal, private information, they would then be less inclined to disclose such information while visiting online social networking sites.