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Zhang, Y.; Liu, J.; Song, S.: ¬The design and evaluation of a nudge-based interface to facilitate consumers' evaluation of online health information credibility (2023)
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- Abstract
- Evaluating the quality of online health information (OHI) is a major challenge facing consumers. We designed PageGraph, an interface that displays quality indicators and associated values for a webpage, based on credibility evaluation models, the nudge theory, and existing empirical research concerning professionals' and consumers' evaluation of OHI quality. A qualitative evaluation of the interface with 16 participants revealed that PageGraph rendered the information and presentation nudges as intended. It provided the participants with easier access to quality indicators, encouraged fresh angles to assess information credibility, provided an evaluation framework, and encouraged validation of initial judgments. We then conducted a quantitative evaluation of the interface involving 60 participants using a between-subject experimental design. The control group used a regular web browser and evaluated the credibility of 12 preselected webpages, whereas the experimental group evaluated the same webpages with the assistance of PageGraph. PageGraph did not significantly influence participants' evaluation results. The results may be attributed to the insufficiency of the saliency and structure of the nudges implemented and the webpage stimuli's lack of sensitivity to the intervention. Future directions for applying nudges to support OHI evaluation were discussed.
- Date
- 22. 6.2023 18:18:34
- Source
- Journal of the Association for Information Science and Technology. 74(2023) no.7, S.828-845
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Jiang, Y.; Meng, R.; Huang, Y.; Lu, W.; Liu, J.: Generating keyphrases for readers : a controllable keyphrase generation framework (2023)
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- Abstract
- With the wide application of keyphrases in many Information Retrieval (IR) and Natural Language Processing (NLP) tasks, automatic keyphrase prediction has been emerging. However, these statistically important phrases are contributing increasingly less to the related tasks because the end-to-end learning mechanism enables models to learn the important semantic information of the text directly. Similarly, keyphrases are of little help for readers to quickly grasp the paper's main idea because the relationship between the keyphrase and the paper is not explicit to readers. Therefore, we propose to generate keyphrases with specific functions for readers to bridge the semantic gap between them and the information producers, and verify the effectiveness of the keyphrase function for assisting users' comprehension with a user experiment. A controllable keyphrase generation framework (the CKPG) that uses the keyphrase function as a control code to generate categorized keyphrases is proposed and implemented based on Transformer, BART, and T5, respectively. For the Computer Science domain, the Macro-avgs of , , and on the Paper with Code dataset are up to 0.680, 0.535, and 0.558, respectively. Our experimental results indicate the effectiveness of the CKPG models.
- Date
- 22. 6.2023 14:55:20
- Source
- Journal of the Association for Information Science and Technology. 74(2023) no.7, S.759-774
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Liu, J.; Liu, C.: Personalization in text information retrieval : a survey (2020)
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- Abstract
- Personalization of information retrieval (PIR) is aimed at tailoring a search toward individual users and user groups by taking account of additional information about users besides their queries. In the past two decades or so, PIR has received extensive attention in both academia and industry. This article surveys the literature of personalization in text retrieval, following a framework for aspects or factors that can be used for personalization. The framework consists of additional information about users that can be explicitly obtained by asking users for their preferences, or implicitly inferred from users' search behaviors. Users' characteristics and contextual factors such as tasks, time, location, etc., can be helpful for personalization. This article also addresses various issues including when to personalize, the evaluation of PIR, privacy, usability, etc. Based on the extensive review, challenges are discussed and directions for future effort are suggested.
- Source
- Journal of the Association for Information Science and Technology. 71(2020) no.3, S.349-369
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Liu, J.; Zhao, J.: More than plain text : censorship deletion in the Chinese social media (2021)
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- Abstract
- Although the Internet allows people to circulate messages using different media, most censorship studies discuss the removal of text content. This article presents a systematic study regarding the censorship of both plain text and multimedia content on the Chinese Internet. By analyzing both censored and surviving posts on the Chinese social media platform Weibo during the 2014 Hong Kong Umbrella Movement, we find that multimedia posts suffered more intensive censorship deletion than plain text posts, with censorship programs being oriented more toward multimedia content like images than the text content of multimedia posts. Our analysis has significant implications for censorship studies, information control, and politics in the "post-text" era.
- Source
- Journal of the Association for Information Science and Technology. 72(2021) no.1, S.18-31
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Jiang, X.; Liu, J.: Extracting the evolutionary backbone of scientific domains : the semantic main path network analysis approach based on citation context analysis (2023)
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- Source
- Journal of the Association for Information Science and Technology. 74(2023) no.5, S.546-569
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Liu, J.; Zhou, Z.; Gao, M.; Tang, J.; Fan, W.: Aspect sentiment mining of short bullet screen comments from online TV series (2023)
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- Source
- Journal of the Association for Information Science and Technology. 74(2023) no.8, S.1026-1045
