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Completed Papers 9: Information Organization and Social Computing
1:30pm - 3:00pm
Session Chair: Vivien Petras, Humboldt-Universitaet zu Berlin
Location:Hankou Hall Location: Third Floor
Recovery and Maintenance: How Women with Eating Disorders Use Instagram
Elizabeth V. Eikey, Kayla M. Booth
The Pennsylvania State University, United States of America
Research tends to consider either the positive or negative impact of technologies on eating disorders but rarely considers how technology can be used to aid in recovery as well as exacerbate users’ conditions. Social media is not overtly harmful or helpful within this context, but rather, Instagram, like other spaces, serves as a double-edged sword that can both help recovery and enable pro-eating disorder behaviors. We conducted semi-structured interviews about ICTs and social networking sites with 16 women with eating disorders. Instagram emerged as the most commonly used ICT. We found Instagram can aid in recovery by helping women: (1) learn about the recovery process, (2) track their own recovery, (3) learn about healthy foods and exercises, and (4) reduce stigma, increase awareness, and create a community for social support. Instagram can also (1) be used to maintain eating disorder symptoms and (2) promote comparisons, which can trigger and exacerbate eating disorders. This research has implications for design, healthcare, and education.
Towards a Graph-based Data Model for Semantics Evolution
1School of Information Management, Wuhan University, China; 2School of Computer Science, Carleton University, Ottawa, Canada; 3State Key Laboratory of Software Engineering, Wuhan University, China
Semantic information comes from the things being recognized and understood gradually, and thus it is often in evolution during the modeling process. Existing semantic models usually describe the objects and the relationships in an application-oriented way, which seldom considers the reuse of the schemas during the semantics evolution. In this paper, we propose a new graph-based semantic data model to overcome the limitation. SemGraph adopts a meaning-oriented approach to specifying the subjective view of the things and uses the certain meta-meaning relations to build a graph-based semantic model. The model is simple but expressive, and is especially fit for the semantics evolution. We introduce the basic concepts and the essential mechanisms of the model, demonstrate its features with examples and compare it with related modeling approaches.
Enrichment of Cross-Lingual Information on Chinese Genealogical Linked Data
1University of Liverpool; 2Xi'an Jiaotong-Liverpool University
With the emergence of non-English Linked Datasets, discrepancy in language has become a major obstacle for cross-lingual access of resources in the Semantic Web. To prevent non-English monolingual Linked Datasets to form ''islands'' in the Web of Data, it is suggested to enrich a further layer of multilingual information on the Linked Open Data cloud. In the domain of culture heritage, enriching cross-lingual information can enhance the multilingual retrieval of cultural heritage resources, and promote international communication in the field. In this article, methods to enrich cross-lingual information for Linked Data are summarized, with a review on the cultural heritage domain. The mobile App Demo, Learn Chinese Surnames, winning the Shanghai Library Open Data Application Development Contest on 2016, is then introduced as a case study, to present the practice of enriching English-described information on a Chinese genealogical Linked Dataset, through consuming multilingual sources in the Linked Open Data cloud. Further in the data validation and conclusion, the issues of data quality and experience of consuming Linked Data are summarized.