Subjects: Library Science，Information Science >> Information Retrieval submitted time 2024-04-18
Abstract： The purpose of this study is to explore the auxiliary strategies of lower grade students in Internet information search tasks to improve their information literacy. Method/Process Based on the information needs expression theory, combined with field investigation and field interview methods, this study analyzed the process of primary school students using Baidu to search for information in the activity of Lighting up the Library . The process model of information search task is established by dividing the search task into stages, which covers the key steps such as task analysis, query construction, search result screening, content interpretation, information extraction and task completion. Result/ Conclusion The study found that lower grade students showed certain information literacy ability in information extraction and utilization, but still needed the help of auxiliary staff. In addition, the study also discusses the experience and evaluation of primary school students’ use of search engines, and finds that they have a very rich experience in using search engines such as Baidu, and give a high evaluation of the search experience of Baidu, and also raises questions from the perspective of primary school students. This study is of great significance for designing reasonable teaching activities and improving elementary school students’ information literacy, and provides a new perspective and data support for future research in related fields.
Peer Review Status:
Awaiting Review
Subjects: Computer Science >> Natural Language Understanding and Machine Translation Subjects: Library Science，Information Science >> Information Retrieval Subjects: Management Science >> Science ology and Management Subjects: Other Disciplines >> Synthetic discipline submitted time 2023-09-22
Abstract： Purpose/significance ChatGPT is a chatbot program developed by OpenAI in the United States. Conversations with ChatGPT can shed light on the scientific research in the age of AI. Method/process Currently, ChatGPT offers users 30 free query credits per day. By creating an outline for the conversation, Chen Yu engaged in a dialog with ChatGPT on various issues of the scientific research. Result/conclusion In the AI era, the AI technology represented by ChatGPT can become a "game-changer" in scientific research. Specifically, AI technology represented by ChatGPT can achieve faster data analysis, hypothesis generation, and decision making, trigger paradigm innovation in scientific research, promote interdisciplinary research, discover new research problems and research directions, lower the "barriers to entry" to scientific research, and promote scientific popularization and knowledge dissemination. At the same time, there are a number of potential risks associated with the use of AI technology represented by ChatGPT in scientific research, including privacy or data security issues, over-reliance on AI technology, rigidity of thinking, stereotyping or even prejudice against certain genders, races, cultures, languages and ideologies, intellectual property rights, workforce adaptation, academic misconduct, and digital hegemony or AI hegemony in the English-speaking world.
Peer Review Status:Awaiting Review
Subjects: Computer Science >> Natural Language Understanding and Machine Translation Subjects: Library Science，Information Science >> Information Retrieval Subjects: Other Disciplines >> Synthetic discipline Subjects: Management Science >> Science ology and Management submitted time 2023-09-22
Abstract： Purpose/significance ChatGPT is a chatbot program developed by OpenAI in the United States. Conversations with ChatGPT can shed light on the scientific research in the age of AI. Method/process Currently, ChatGPT offers users 30 free query credits per day. By creating an outline for the conversation, Chen Yu engaged in a dialog with ChatGPT on various issues of the scientific research. Result/conclusion In the AI era, the AI technology represented by ChatGPT can become a "game-changer" in scientific research. Specifically, AI technology represented by ChatGPT can achieve faster data analysis, hypothesis generation, and decision making, trigger paradigm innovation in scientific research, promote interdisciplinary research, discover new research problems and research directions, lower the "barriers to entry" to scientific research, and promote scientific popularization and knowledge dissemination. At the same time, there are a number of potential risks associated with the use of AI technology represented by ChatGPT in scientific research, including privacy or data security issues, over-reliance on AI technology, rigidity of thinking, stereotyping or even prejudice against certain genders, races, cultures, languages and ideologies, intellectual property rights, workforce adaptation, academic misconduct, and digital hegemony or AI hegemony in the English-speaking world.
Peer Review Status:Awaiting Review
Subjects: Library Science，Information Science >> Information Retrieval submitted time 2023-08-28
Abstract： Purpose/Significance Explore the similarities and differences between ChatGPT generation and Chinese paper abstracts written by scholars, and analyze the differences in content characteristics between the two, providing reference for AI generated academic paper detection and related research. Method/Process Firstly, taking the field of information resource management as an example, we extracted 500 highly cited papers from library science, information science, and archival science in the past three years. Based on the obtained paper titles, we used the Prompt method to apply the ChatGPT tool to generate corresponding abstract texts and construct a dataset; Secondly, 9 machine learning and deep learning algorithms were used to classify and detect abstract texts generated by ChatGPT and written by scholars; Finally, analyze the similarities and differences between the two from multiple perspectives, including text features, topic models, and ROUGE evaluation, in order to reveal the similarities and differences between the two. Result/Conclusion Mainstream machine learning and deep learning algorithms trained on datasets can effectively distinguish whether abstracts are generated by AI or written by scholars, with BERT and ERNIE performing the best, while RF and Xgboost perform the best among machine learning algorithms. The number of abstract characters and sentences generated by ChatGPT is higher than that written by scholars, and the keywords are mostly template based transitional words; The themes of the two texts are mostly the same, but there are differences in themes such as "disciplinary system" and "digital humanities"; The quantitative analysis of ROUGE and cosine similarity indicates that the abstracts generated by ChatGPT have a significant "resemblance" rather than a "resemblance" to the abstract texts written by scholars.
Peer Review Status:Awaiting Review
Subjects: Library Science，Information Science >> Information Retrieval submitted time 2023-03-17
Abstract： 目的/意义 受高校间信息交流方式和频率的限制，加之疫情的影响，高校图书馆之间无法全面快捷的了解到同行间的新闻资讯及资源动态等信息（以下简称资讯动态）。 方法/过程 分析统计了国内C9高校的图书馆门户网站页面结构，编写热插拔式的网络爬虫抓取资讯动态相关页面内容，同时避免对对方网络设备和流量造成压力和影响，并对抓取到的文本内容进行信息提取，取出关键词并绘制词云图。 结果/结论 以禅道开源框架为基础，构建信息查询和展示平台，供馆领导及采访馆员关注同行资讯动态。并对此应用场景扩展到国内外更多的高校进行了总结与展望。
Peer Review Status:Awaiting Review
Subjects: Library Science，Information Science >> Information Retrieval submitted time 2023-02-09
Abstract：
[Background and purpose] Author recognition is developing towards the use of multilevel features. Compared with stylistic features, thematic features are still a few in the research and application of author recognition, especially for Chinese social media texts. At the same time, the research on the use of topic features focuses more on the innovation of the extraction technology and methods of topic features, but not on the identified topics and the application methods of topic features. Therefore, the basic purpose of this study is to study the use of topic features in the author recognition of Chinese social media texts, and further develop strategies to identify and screen the core topics in the topic features, optimize the use of topic features, so as to improve the use effect of topic features in the author recognition. [Methods] The research first uses the LDA topic model to extract the academic topics and social topics of the candidate authors, and then uses Word2vec to develop a merge screening strategy to identify and represent the core topics, and finally uses N-gram features and similarity calculation to achieve author recognition. [Results] The experimental results showed that the thematic features had a certain positive effect on the author's recognition in the corpus of this study, and the strategies and applications related to the core thematic features proposed in this study could also optimize the use of thematic features.
Peer Review Status:Awaiting Review
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