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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.
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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.
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Zhang, X.; Li, Y.; Liu, J.; Zhang, Y.: Effects of interaction design in digital libraries on user interactions (2008)
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- Abstract
- Purpose - This study aims to investigate the effects of different search and browse features in digital libraries (DLs) on task interactions, and what features would lead to poor user experience. Design/methodology/approach - Three operational DLs: ACM, IEEE CS, and IEEE Xplore are used in this study. These three DLs present different features in their search and browsing designs. Two information-seeking tasks are constructed: one search task and one browsing task. An experiment was conducted in a usability laboratory. Data from 35 participants are collected on a set of measures for user interactions. Findings - The results demonstrate significant differences in many aspects of the user interactions between the three DLs. For both search and browse designs, the features that lead to poor user interactions are identified. Research limitations/implications - User interactions are affected by specific design features in DLs. Some of the design features may lead to poor user performance and should be improved. The study was limited mainly in the variety and the number of tasks used. Originality/value - The study provided empirical evidence to the effects of interaction design features in DLs on user interactions and performance. The results contribute to our knowledge about DL designs in general and about the three operational DLs in particular.
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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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- Abstract
- Bullet screen comments (BSCs) are user-generated short comments that appear as real-time overlays on many video platforms, expressing the audience opinions and emotions about different aspects of the ongoing video. Unlike traditional long comments after a show, BSCs are often incomplete, ambiguous in context, and correlated over time. Current studies in sentiment analysis of BSCs rarely address these challenges, motivating us to develop an aspect-level sentiment analysis framework. Our framework, BSCNET, is a pre-trained language encoder-based deep neural classifier designed to enhance semantic understanding. A novel neighbor context construction method is proposed to uncover latent contextual correlation among BSCs over time, and we also incorporate semi-supervised learning to reduce labeling costs. The framework increases F1 (Macro) and accuracy by up to 10% and 10.2%, respectively. Additionally, we have developed two novel downstream tasks. The first is noisy BSCs identification, which reached F1 (Macro) and accuracy of 90.1% and 98.3%, respectively, through fine-tuning the BSCNET. The second is the prediction of future episode popularity, where the MAPE is reduced by 11%-19.0% when incorporating sentiment features. Overall, this study provides a methodology reference for aspect-level sentiment analysis of BSCs and highlights its potential for viewing experience or forthcoming content optimization.
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Liu, J.: CIP in China : the development and status quo (1996)
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- Source
- Cataloging and classification quarterly. 22(1996) no.1, S.69-76
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Zhou, D.; Lawless, S.; Wu, X.; Zhao, W.; Liu, J.: ¬A study of user profile representation for personalized cross-language information retrieval (2016)
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- Date
- 20. 1.2015 18:30:22
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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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- Date
- 22. 6.2023 18:18:34
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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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- Date
- 22. 6.2023 14:55:20
