Search (7 results, page 1 of 1)

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Abstract

Social influence has a great impact on human behavior, which has been widely investigated in various research fields. Even so, it has rarely been investigated in the online health community. In this paper, we focus on the multichannel access in online health communities, defining social influence as the average degree of multichannel access to a physician's colleagues. Based on the multinomial logistic regression model, we examined the direct effects of social influence and patients' rating to multichannel access. In addition, we explored the moderating effect of social influence on the relationship between patients' rating and multichannel access in online health communities. The results of the experiment and robustness testing support the propositions that social influence and patients' rating significantly and positively affect multichannel access in an online health community. The moderating effect of social influence is negative and significantly influences the accessible channels provided by the focal physician. This research contributes to the literature concerning online health communities, social influence, and multichannel access; it also has practical implications.

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Abstract

Social media communities have emerged recently as open and free communication platforms to support real-time information sharing among members. Drawing on social capital theories, we develop a theoretical model to investigate how the two types of social capital (bonding and bridging) contribute to the individual and collective well-being of virtual communities through information exchange. Research hypotheses were tested through survey instruments and computer archive data of 475 members of a large social network site during the Wenchuan earthquake (2008) in China. We find that bonding has a positive and significant impact on bridging. Both bonding and bridging have positive and significant impacts on information quality, but not on information quantity. Results also suggest that information quality is more critical to individuals and collective well-being than information quantity after a disaster.

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Abstract

Social media is frequently used as a platform for the exchange of information and opinions as well as propaganda dissemination. But online content can be misused for the distribution of illicit information, such as violent postings in web forums. Illicit content is highly distributed in social media, while non-illicit content is unspecific and topically diverse. It is costly and time consuming to label a large amount of illicit content (positive examples) and non-illicit content (negative examples) to train classification systems. Nevertheless, it is relatively easy to obtain large volumes of unlabeled content in social media. In this article, an artificial immune system-based technique is presented to address the difficulties in the illicit content identification in social media. Inspired by the positive selection principle in the immune system, we designed a novel labeling heuristic based on partially supervised learning to extract high-quality positive and negative examples from unlabeled datasets. The empirical evaluation results from two large hate group web forums suggest that our proposed approach generally outperforms the benchmark techniques and exhibits more stable performance.

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Abstract

Websites offer an unobtrusive data source for developing and analyzing information about various types of social science phenomena. In this paper, we provide a methodological resource for social scientists looking to expand their toolkit using unstructured web-based text, and in particular, with the Wayback Machine, to access historical website data. After providing a literature review of existing research that uses the Wayback Machine, we put forward a step-by-step description of how the analyst can design a research project using archived websites. We draw on the example of a project that analyzes indicators of innovation activities and strategies in 300 U.S. small- and medium-sized enterprises in green goods industries. We present six steps to access historical Wayback website data: (a) sampling, (b) organizing and defining the boundaries of the web crawl, (c) crawling, (d) website variable operationalization, (e) integration with other data sources, and (f) analysis. Although our examples draw on specific types of firms in green goods industries, the method can be generalized to other areas of research. In discussing the limitations and benefits of using the Wayback Machine, we note that both machine and human effort are essential to developing a high-quality data set from archived web information.

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Abstract

Friend request acceptance and information disclosure constitute 2 important privacy decisions for users to control the flow of their personal information in social network sites (SNSs). These decisions are greatly influenced by contextual characteristics of the request. However, the contextual influence may not be uniform among users with different levels of privacy concerns. In this study, we hypothesize that users with higher privacy concerns may consider contextual factors differently from those with lower privacy concerns. By conducting a scenario-based survey study and structural equation modeling, we verify the interaction effects between privacy concerns and contextual factors. We additionally find that users' perceived risk towards the requester mediates the effect of context and privacy concerns. These results extend our understanding about the cognitive process behind privacy decision making in SNSs. The interaction effects suggest strategies for SNS providers to predict user's friend request acceptance and to customize context-aware privacy decision support based on users' different privacy attitudes.

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Abstract

The aim of this paper is to visualize the history of evidence-based medicine (EBM) and to examine the characteristics of EBM development in China and the West. We searched the Web of Science and the Chinese National Knowledge Infrastructure database for papers related to EBM. We applied information visualization techniques, citation analysis, cocitation analysis, cocitation cluster analysis, and network analysis to construct historiographies, themes networks, and chronological theme maps regarding EBM in China and the West. EBM appeared to develop in 4 stages: incubation (1972-1992 in the West vs. 1982-1999 in China), initiation (1992-1993 vs. 1999-2000), rapid development (1993-2000 vs. 2000-2004), and stable distribution (2000 onwards vs. 2004 onwards). Although there was a lag in EBM initiation in China compared with the West, the pace of development appeared similar. Our study shows that important differences exist in research themes, domain structures, and development depth, and in the speed of adoption between China and the West. In the West, efforts in EBM have shifted from education to practice, and from the quality of evidence to its translation. In China, there was a similar shift from education to practice, and from production of evidence to its translation. In addition, this concept has diffused to other healthcare areas, leading to the development of evidence-based traditional Chinese medicine, evidence-based nursing, and evidence-based policy making.

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Abstract

This article studies the impact of differences in citation practices at the subfield, or Web of Science subject category level, using the model introduced in Crespo, Li, and Ruiz-Castillo (2013a), according to which the number of citations received by an article depends on its underlying scientific influence and the field to which it belongs. We use the same Thomson Reuters data set of about 4.4 million articles used in Crespo et al. (2013a) to analyze 22 broad fields. The main results are the following: First, when the classification system goes from 22 fields to 219 subfields the effect on citation inequality of differences in citation practices increases from ?14% at the field level to 18% at the subfield level. Second, we estimate a set of exchange rates (ERs) over a wide [660, 978] citation quantile interval to express the citation counts of articles into the equivalent counts in the all-sciences case. In the fractional case, for example, we find that in 187 of 219 subfields the ERs are reliable in the sense that the coefficient of variation is smaller than or equal to 0.10. Third, in the fractional case the normalization of the raw data using the ERs (or subfield mean citations) as normalization factors reduces the importance of the differences in citation practices from 18% to 3.8% (3.4%) of overall citation inequality. Fourth, the results in the fractional case are essentially replicated when we adopt a multiplicative approach.