Search (4 results, page 1 of 1)

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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.

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Abstract

This article reports on a study that aimed at simplifying a search task difficulty reason scheme. Liu, Kim, and Creel (2015) (denoted LKC15) developed a 21-item search task difficulty reason scheme using a controlled laboratory experiment. The current study simplified the scheme through another experiment that followed the same design as LKC15 and involved 32 university students. The study had one added questionnaire item that provided a list of the 21 difficulty reasons in the multiple-choice format. By comparing the current study with LKC15, a concept of primary top difficulty reasons was proposed, which reasonably simplified the 21-item scheme to an 8-item top reason list. This limited number of reasons is more manageable and makes it feasible for search systems to predict task difficulty reasons from observable user behaviors, which builds the basis for systems to improve user satisfaction based on predicted search difficulty reasons.

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Abstract

This study explores the relationships between work task and interactive information search behavior. Work task was conceptualized based on a faceted classification of task. An experiment was conducted with six work-task types and simulated work-task situations assigned to 24 participants. The results indicate that users present different behavior patterns to approach useful information for different work tasks: They select information systems to search based on the work tasks at hand, different work tasks motivate different types of search tasks, and different facets controlled in the study play different roles in shaping users' interactive information search behavior. The results provide empirical evidence to support the view that work tasks and search tasks play different roles in a user's interaction with information systems and that work task should be considered as a multifaceted variable. The findings provide a possibility to make predictions of a user's information search behavior from his or her work task, and vice versa. Thus, this study sheds light on task-based information seeking and search, and has implications in adaptive information retrieval (IR) and personalization of IR.

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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.