Libraries at University of Nebraska-Lincoln


Date of this Version

Fall 10-1-2019


Aarts, H., & Dijksterhuis, A. (2003). The silence of the library: environment, situational norm, and social behavior. Journal of personality and social psychology, 84(1), 18.

Ali, K., Hamilton, M., Thevathayan, C., & Zhang, X. (2018a). Big Social Data as a Service: A Service Composition Framework for Social Information Service Analysis. Paper presented at the International Conference on Web Services.

Ali, K., Hamilton, M., Thevathayan, C., & Zhang, X. (2018b). Social Information Services: A Service Oriented Analysis of Social Media. Paper presented at the International Conference on Web Services.

Borgatti, S. P., Everett, M. G., & Johnson, J. C. (2018). Analyzing social networks: Sage.

Brusilovsky, P. (2008). Social information access: the other side of the social web. Paper presented at the International Conference on Current Trends in Theory and Practice of Computer Science.

Brusilovsky, P., & He, D. (2018). Introduction to social information access. In Social Information Access (pp. 1-18): Springer.

Dahl, C. D., Wyss, C., Zuberbühler, K., & Bachmann, I. (2018). Social information in equine movement gestalts. Animal Cognition, 21(4), 583-594.

De Nooy, W., Mrvar, A., & Batagelj, V. (2018). Exploratory social network analysis with Pajek: Cambridge University Press.

Han, S., & He, D. (2018). Network-based social search. In Social Information Access (pp. 277-309): Springer.

Howard, H. A., Huber, S. E., Moore, E. A., & Carter, L. (2018). Academic Libraries on Social Media: Finding the Students and the Information They Want. Academic Libraries on Social Media: Finding the Students and the Information They Want.

Johnson, P. (2018). Fundamentals of collection development and management: American Library Association.

Kaur, R., & Sharma, A. (2018). 21 st Century Library Professionals in Dynamic Role in Digital Era. Library of Progress-Library Science, Information Technology & Computer, 38(1).

Knijnenburg, B. P. (2018). Privacy in social information access. In Social Information Access (pp. 19-74): Springer.

Lee, Y.-A., Lionnet, S., Kato, A., & Goto, Y. (2018). Dopamine-dependent social information processing in non-human primates. Psychopharmacology, 235(4), 1141-1149.

Lin, P. (2010). Information literacy barriers: language use and social structure. Library Hi Tech, 28(4), 548-568.

Luebbe, A. M., Bell, D. J., Allwood, M. A., Swenson, L. P., & Early, M. C. (2010). Social information processing in children: Specific relations to anxiety, depression, and affect. Journal of Clinical Child & Adolescent Psychology, 39(3), 386-399.

MARQUES, L. K. d. S., & Vidigal, F. (2018). Prosumers and social networks as marketing information sources. An analysis from the perspective of competitive intelligence in Brazilian companies. Transinformação, 30(1), 1-14.

Mazza, M., Mariano, M., Peretti, S., Masedu, F., Pino, M. C., & Valenti, M. (2017). The role of theory of mind on social information processing in children with autism spectrum disorders: A mediation analysis. Journal of autism and developmental disorders, 47(5), 1369-1379.

Shi, J., Lai, K. K., Hu, P., & Chen, G. (2018). Factors dominating individual information disseminating behavior on social networking sites. Information Technology and Management, 19(2), 121-139.

Srinivasan, J., Von Reuss, S. H., Bose, N., Zaslaver, A., Mahanti, P., Ho, M. C., . . . Schroeder, F. C. (2012). A modular library of small molecule signals regulates social behaviors in Caenorhabditis elegans. PLoS biology, 10(1), e1001237.

Wentura, D., Rothermund, K., & Bak, P. (2000). Automatic vigilance: The attention-grabbing power of approach-and avoidance-related social information. Journal of personality and social psychology, 78(6), 1024.

Yue, Z., & He, D. (2018). Collaborative information search. In Social Information Access (pp. 108-141): Springer.


Introduction: Access to social information focuses on those technologies that put the users in interaction with the information, to provide the users more accessibility to the information.

Objective: The analysis of intellectual structure of the published studies of social information field in the Web of Science (WoS) database according to bibliometric analysis and centrality indicators in WoS database in the period 1983 to 2018, is done through examining the effectiveness and impressionability of this field to recognize its studied fields and effective factors.

Methodology: method of the research was descriptive that used bibliometric approaches based on the scientific data in WoS database; The common techniques such as co-word and co-author were used. The social information extracted data were analyzed by social network analysis centrality indicators of VOSviewer, Excel, and UCINET software.

Results: The results shown that the most of these articles within social information field were published in USA, Germany, England, France, and China, and also some research outputs have been done in that fields in Korea and Iran. According to the scientific outputs of researchers in the WoS database, on the period 1983 to 2018, USA took the first place with 1496 articles. Authors such as Sheldon, Lalande, Webster, Karius, Nocera, Forceman, and Laaksonen have had the most cooperation in the production of social information field scientific outputs. Based on co-words analysis of web of science category (WC) and subject categories (SC), social information area in this study was divided into four clusters which their topics included social recognition, social behavior confronted with outer environment, social networks, learning and social information processing in human and software. Also, high degree into different centrality measure is related to Psychology, Telecommunication, behavioral sciences, evolutionary biology, and psychology, multidisciplinary in WC and SC.



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