2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL)最新文献_第4页

Understanding the Contributions of Junior Researchers at Software-Engineering Conferences 了解初级研究人员在软件工程会议上的贡献

2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL) Pub Date : 2021-09-01 DOI: 10.1109/JCDL52503.2021.00056

Rand Alchokr, J. Krüger, Yusra Shakeel, G. Saake, Thomas Leich

引用次数: 2

Girl with a Pearl Earring: Supporting ‘Close Reading’ of Art in a Digital Library 戴珍珠耳环的女孩:支持数字图书馆艺术的“细读”

2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL) Pub Date : 2021-09-01 DOI: 10.1109/JCDL52503.2021.00048

S. Cunningham, C. Short

引用次数: 0

Do You Think It's Biased? How To Ask For The Perception Of Media Bias 你认为它有偏见吗?如何要求媒体偏见的感知

2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL) Pub Date : 2021-09-01 DOI: 10.1109/JCDL52503.2021.00018

Timo Spinde, Christina Kreuter, W. Gaissmaier, Felix Hamborg, Bela Gipp, H. Giese

{"title":"Do You Think It's Biased? How To Ask For The Perception Of Media Bias","authors":"Timo Spinde, Christina Kreuter, W. Gaissmaier, Felix Hamborg, Bela Gipp, H. Giese","doi":"10.1109/JCDL52503.2021.00018","DOIUrl":"https://doi.org/10.1109/JCDL52503.2021.00018","url":null,"abstract":"Media coverage possesses a substantial effect on the public perception of events. The way media frames events can significantly alter the beliefs and perceptions of our society. Nevertheless, nearly all media outlets are known to report news in a biased way. While such bias can be introduced by altering the word choice or omitting information, the perception of bias also varies largely depending on a reader's personal background. Therefore, media bias is a very complex construct to identify and analyze. Even though media bias has been the subject of many studies, previous assessment strategies are oversimplified, lack overlap and empirical evaluation. Thus, this study aims to develop a scale that can be used as a reliable standard to evaluate article bias. To name an example: Intending to measure bias in a news article, should we ask, “How biased is the article?” or should we instead ask, “How did the article treat the American president?”. We conducted a literature search to find 824 relevant questions about text perception in previous research on the topic. In a multi-iterative process, we summarized and condensed these questions semantically to conclude a complete and representative set of possible question types about bias. The final set consisted of 25 questions with varying answering formats, 17 questions using semantic differentials, and six ratings of feelings. We tested each of the questions on 190 articles with overall 663 participants to identify how well the questions measure an article's perceived bias. Our results show that 21 final items are suitable and reliable for measuring the perception of media bias. We publish the final set of questions on http://bias-guestion-tree.gipplab.org/.","PeriodicalId":112400,"journal":{"name":"2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL)","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121725663","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 13

2021 ACM/IEEE Joint Conference on Digital Libraries 2021年ACM/IEEE数字图书馆联合会议

2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL) Pub Date : 2021-09-01 DOI: 10.1109/jcdl52503.2021.00001

引用次数: 0

Introduction to and Hands-On Use Cases with HathiTrust Research Center's Extracted Features 2.0 Dataset 介绍和动手用例与HathiTrust研究中心的提取特征2.0数据集

2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL) Pub Date : 2021-09-01 DOI: 10.1109/JCDL52503.2021.00073

Ryan Dubnicek, Deren Kudeki

引用次数: 0

User-Centred Application for Modeling Journeys in Digital Libraries 以用户为中心的数字化图书馆旅程建模应用

2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL) Pub Date : 2021-09-01 DOI: 10.1109/JCDL52503.2021.00057

Marwa Trabelsi, Cyrille Suire, Jacques Morcos, R. Champagnat

引用次数: 0

A Comparative Analysis of Article Recommendation Platforms 文章推荐平台的比较分析

2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL) Pub Date : 2021-09-01 DOI: 10.1109/JCDL52503.2021.00012

Rand Alchokr, J. Krüger, G. Saake, Thomas Leich

{"title":"A Comparative Analysis of Article Recommendation Platforms","authors":"Rand Alchokr, J. Krüger, G. Saake, Thomas Leich","doi":"10.1109/JCDL52503.2021.00012","DOIUrl":"https://doi.org/10.1109/JCDL52503.2021.00012","url":null,"abstract":"Even though it is a controversial matter, research (e.g., publications, projects, researchers) is regularly evaluated based on some form of scientific impact. Particularly citation counts and metrics building on them (e.g., impact factor, h-index) are established for this purpose, despite missing evidence that they are reasonable and researchers rightfully criticizing their use. Several ideas aim to tackle such problems by proposing to abandon metrics-based evaluations or suggesting new methods that cover other properties, for instance, through Altmetrics or Article Recommendation Platforms (ARPs). ARPs are particularly interesting, since they encourage their community to decide which publications are important, for instance, based on recommendations, post-publication reviews, comments, or discussions. In this paper, we report a comparative analysis of 11 ARPs, which utilize human expertise to assess the quality, correctness, and potential importance of a publication. We compare the different properties, pros, and cons of the ARPs, and discuss the adoption potential for computer science. We find that some of the platforms' features are challenging to understand, but they enforce the trend of involving humans instead of metrics for evaluating research.","PeriodicalId":112400,"journal":{"name":"2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123858193","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

A Deep Neural Architecture for Decision-Aware Meta-Review Generation 决策感知元评论生成的深度神经体系结构

2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL) Pub Date : 2021-09-01 DOI: 10.1109/JCDL52503.2021.00064

Asheesh Kumar, Tirthankar Ghosal, Asif Ekbal

{"title":"A Deep Neural Architecture for Decision-Aware Meta-Review Generation","authors":"Asheesh Kumar, Tirthankar Ghosal, Asif Ekbal","doi":"10.1109/JCDL52503.2021.00064","DOIUrl":"https://doi.org/10.1109/JCDL52503.2021.00064","url":null,"abstract":"Automatically generating meta-reviews from peer-reviews is a new and challenging task. Although close, the task is not precisely summarizing the peer-reviews. Usually, a conference chair or a journal editor writes a meta-review after going through the reviews written by the appointed reviewers, rounds of discussions with them, finally arriving at a consensus on the paper's fate. In essence, the meta-review texts are decision-aware, i.e., the meta reviewer already forms the decision before writing the meta-review, and the corresponding text conforms to that decision. We leverage this seed idea and design a deep neural architecture to generate decision-aware meta-reviews in this work. We propose a multi-encoder transformer network for peer-review decision prediction and subsequent meta-review generation. We analyze our output quantitatively and qualitatively and argue that quantitative text summarization metrics are not suitable for evaluating the generated meta-reviews. Our proposed model performs comparably with the recent state-of-the-art text summarization approaches. Qualitative evaluation of our model-generated output is encouraging on an open access peer reviews dataset that we curate from the open review platform. We make our data and codes available11https://www.iitp.ac.in/~ai-nlp-ml/resources.html# decision-aware-meta-review.","PeriodicalId":112400,"journal":{"name":"2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121810126","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7

Academic Storage Cluster 学术存储集群

2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL) Pub Date : 2021-09-01 DOI: 10.1109/JCDL52503.2021.00034

Alexander von Tottleben, Cornelius Ihle, M. Schubotz, Bela Gipp

引用次数: 0

Analyzing Unconstrained Reading Patterns of Digital Documents Using Eye Tracking 利用眼动追踪分析数字文档的无约束阅读模式

2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL) Pub Date : 2021-09-01 DOI: 10.1109/JCDL52503.2021.00036

Bhanuka Mahanama, Gavindya Jayawardena, S. Jayarathna

引用次数: 4