The Experts below are selected from a list of 270 Experts worldwide ranked by ideXlab platform
Robert E. Kraut - One of the best experts on this subject based on the ideXlab platform.
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Modeling Self-Disclosure in Social Networking Sites
Proceedings of the 19th ACM Conference on Computer-Supported Cooperative Work & Social Computing - CSCW '16, 2016Co-Authors: Yi-chia Wang, Moira Burke, Robert E. KrautAbstract:Social networking sites (SNSs) offer users a platform to build and maintain social connections. Understanding when people feel comfortable sharing information about themselves on SNSs is critical to a good user experience, because Self-Disclosure helps maintain friendships and increase relationship closeness. This observational research develops a machine learning model to measure Self-Disclosure in SNSs and uses it to understand the contexts where it is higher or lower. Features include emotional valence, social distance between the poster and people mentioned in the post, the language similarity between the post and the community and post topic. To validate the model and advance our understanding about online Self-Disclosure, we applied it to de-identified, aggregated status updates from Facebook users. Results show that women Self-disclose more than men. People with a stronger desire to manage impressions Self-disclose less. Network size is negatively associated with Self-Disclosure, while tie strength and network density are positively associated.
Marko Holbl - One of the best experts on this subject based on the ideXlab platform.
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privacy antecedents for sns Self Disclosure
Computers in Human Behavior, 2015Co-Authors: Lili Nemec Zlatolas, Tatjana Welzer, Marjan Hericko, Marko HolblAbstract:Social networking sites privacy issues and Self-Disclosure are examined.A research model of privacy issues and Self-Disclosure is built.Structural equations modeling is used to assess the model fit.Path analysis is done to analyze hypothesis whereas 11 out of 14 are accepted.Final model shows privacy and its shaping of Self-Disclosure in Facebook. In recent years, social networking sites have spread rapidly, raising new issues in terms of privacy and Self-Disclosure online. For a better understanding of how privacy issues determine Self-Disclosure, a model which includes privacy awareness, privacy social norms, privacy policy, privacy control, privacy value, privacy concerns and Self-Disclosure was built. A total of 661 respondents participated in an online survey and a structural equation modeling was used to evaluate the model. The findings indicated a significant relationship between privacy value/privacy concerns and Self-Disclosure, privacy awareness and privacy concerns/Self-Disclosure, privacy social norms and privacy value/Self-Disclosure, privacy policy and privacy value/privacy concerns/Self-Disclosure, privacy control and privacy value/privacy concerns. The model from the study should contribute new knowledge concerning privacy issues and their shaping of Self-Disclosure on social networking sites. It could also help networking sites service providers understand how to encourage users to disclose more information.
Yi-chia Wang - One of the best experts on this subject based on the ideXlab platform.
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Modeling Self-Disclosure in Social Networking Sites
Proceedings of the 19th ACM Conference on Computer-Supported Cooperative Work & Social Computing - CSCW '16, 2016Co-Authors: Yi-chia Wang, Moira Burke, Robert E. KrautAbstract:Social networking sites (SNSs) offer users a platform to build and maintain social connections. Understanding when people feel comfortable sharing information about themselves on SNSs is critical to a good user experience, because Self-Disclosure helps maintain friendships and increase relationship closeness. This observational research develops a machine learning model to measure Self-Disclosure in SNSs and uses it to understand the contexts where it is higher or lower. Features include emotional valence, social distance between the poster and people mentioned in the post, the language similarity between the post and the community and post topic. To validate the model and advance our understanding about online Self-Disclosure, we applied it to de-identified, aggregated status updates from Facebook users. Results show that women Self-disclose more than men. People with a stronger desire to manage impressions Self-disclose less. Network size is negatively associated with Self-Disclosure, while tie strength and network density are positively associated.
Lili Nemec Zlatolas - One of the best experts on this subject based on the ideXlab platform.
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privacy antecedents for sns Self Disclosure
Computers in Human Behavior, 2015Co-Authors: Lili Nemec Zlatolas, Tatjana Welzer, Marjan Hericko, Marko HolblAbstract:Social networking sites privacy issues and Self-Disclosure are examined.A research model of privacy issues and Self-Disclosure is built.Structural equations modeling is used to assess the model fit.Path analysis is done to analyze hypothesis whereas 11 out of 14 are accepted.Final model shows privacy and its shaping of Self-Disclosure in Facebook. In recent years, social networking sites have spread rapidly, raising new issues in terms of privacy and Self-Disclosure online. For a better understanding of how privacy issues determine Self-Disclosure, a model which includes privacy awareness, privacy social norms, privacy policy, privacy control, privacy value, privacy concerns and Self-Disclosure was built. A total of 661 respondents participated in an online survey and a structural equation modeling was used to evaluate the model. The findings indicated a significant relationship between privacy value/privacy concerns and Self-Disclosure, privacy awareness and privacy concerns/Self-Disclosure, privacy social norms and privacy value/Self-Disclosure, privacy policy and privacy value/privacy concerns/Self-Disclosure, privacy control and privacy value/privacy concerns. The model from the study should contribute new knowledge concerning privacy issues and their shaping of Self-Disclosure on social networking sites. It could also help networking sites service providers understand how to encourage users to disclose more information.
Moira Burke - One of the best experts on this subject based on the ideXlab platform.
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Modeling Self-Disclosure in Social Networking Sites
Proceedings of the 19th ACM Conference on Computer-Supported Cooperative Work & Social Computing - CSCW '16, 2016Co-Authors: Yi-chia Wang, Moira Burke, Robert E. KrautAbstract:Social networking sites (SNSs) offer users a platform to build and maintain social connections. Understanding when people feel comfortable sharing information about themselves on SNSs is critical to a good user experience, because Self-Disclosure helps maintain friendships and increase relationship closeness. This observational research develops a machine learning model to measure Self-Disclosure in SNSs and uses it to understand the contexts where it is higher or lower. Features include emotional valence, social distance between the poster and people mentioned in the post, the language similarity between the post and the community and post topic. To validate the model and advance our understanding about online Self-Disclosure, we applied it to de-identified, aggregated status updates from Facebook users. Results show that women Self-disclose more than men. People with a stronger desire to manage impressions Self-disclose less. Network size is negatively associated with Self-Disclosure, while tie strength and network density are positively associated.