Search (5 results, page 1 of 1)

  • × author_ss:"Liu, J."
  • × year_i:[2020 TO 2030}
  1. Liu, J.; Zhao, J.: More than plain text : censorship deletion in the Chinese social media (2021) 0.02 
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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.
  2. Liu, J.; Liu, C.: Personalization in text information retrieval : a survey (2020) 0.01 
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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.
  3. Liu, J.; Zhou, Z.; Gao, M.; Tang, J.; Fan, W.: Aspect sentiment mining of short bullet screen comments from online TV series (2023) 0.01 
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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.
  4. 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) 0.00 
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    Date
    22. 6.2023 18:18:34
  5. Jiang, Y.; Meng, R.; Huang, Y.; Lu, W.; Liu, J.: Generating keyphrases for readers : a controllable keyphrase generation framework (2023) 0.00 
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    Date
    22. 6.2023 14:55:20

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