Companion Proceedings of the Web Conference 2021最新文献_第3页

Political Polarization and Platform Migration:: A Study of Parler and Twitter Usage by United States of America Congress Members 政治两极分化和平台迁移:美国国会议员使用Parler和Twitter的研究

Companion Proceedings of the Web Conference 2021 Pub Date : 2021-04-19 DOI: 10.1145/3442442.3452305

Jacqueline M. Otala, Gillian Kurtic, Isabella Grasso, Yu Liu, Jeanna Neefe Matthews, Golshan Madraki

{"title":"Political Polarization and Platform Migration:: A Study of Parler and Twitter Usage by United States of America Congress Members","authors":"Jacqueline M. Otala, Gillian Kurtic, Isabella Grasso, Yu Liu, Jeanna Neefe Matthews, Golshan Madraki","doi":"10.1145/3442442.3452305","DOIUrl":"https://doi.org/10.1145/3442442.3452305","url":null,"abstract":"Growing dissatisfaction with platform governance decisions at major social media platforms like Twitter, Facebook, and Instagram has led to a number of substantial efforts, originating both on the political right and the political left, to shift to new platforms. In this paper, we examine one of the most impactful of these platform migration efforts, a recent effort primarily on the political right to shift from Twitter to Parler in response to Twitter's increased efforts to flag misinformation in the lead up to the 2020 election in the US. As a case study, we analyze the usage of Parler by all members of the United States Congress and compare that to their usage of Twitter. Even though usage of Parler, even at its peak, was only a small percentage of Twitter usage, Parler usage has been impactful. Specifically, it was linked to the planning of the January 6, 2021 attack on the United States Capitol building. Going forward, Parler itself may not have a large and lasting impact, but it offers important lessons about the relationship between political polarization, platform migration, and the real-world political impacts of platform governance decisions and the splintering of our media landscape.","PeriodicalId":129420,"journal":{"name":"Companion Proceedings of the Web Conference 2021","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123953050","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: 17

SciBiD: Novel Scientometrics and NoSQL-enabled Scalable and domain-specific Analysis of Big Scholar Data 新科学计量学和支持nosql的可扩展和特定领域的大学者数据分析

Companion Proceedings of the Web Conference 2021 Pub Date : 2021-04-19 DOI: 10.1145/3442442.3453544

M. Bohlouli, Jonathan Hermann, Fabian Sunnus

引用次数: 0

Tweet Sentiment Analysis of the 2020 U.S. Presidential Election 2020年美国总统大选推特情绪分析

Companion Proceedings of the Web Conference 2021 Pub Date : 2021-04-19 DOI: 10.1145/3442442.3452322

Ethan Xia, Han Yue, Hongfu Liu

引用次数: 15

Learning Spatio-Temporal Behavioural Representations for Urban Activity Forecasting 城市活动预测的时空行为表征学习

Companion Proceedings of the Web Conference 2021 Pub Date : 2021-04-19 DOI: 10.1145/3442442.3451892

F. Salim

引用次数: 1

Marketing Communications and the Semantic Web: Theoretical Intersections and Practical Implications 营销传播与语义网:理论交叉点与实践意义

Companion Proceedings of the Web Conference 2021 Pub Date : 2021-04-19 DOI: 10.1145/3442442.3453704

Teodora Petkova

引用次数: 0

A Comparison of Pre-Trained Language Models for Multi-Class Text Classification in the Financial Domain 金融领域多类文本分类的预训练语言模型比较

Companion Proceedings of the Web Conference 2021 Pub Date : 2021-04-19 DOI: 10.1145/3442442.3451375

Yusuf Arslan, Kevin Allix, Lisa Veiber, Cedric Lothritz, Tegawendé F. Bissyandé, Jacques Klein, A. Goujon

{"title":"A Comparison of Pre-Trained Language Models for Multi-Class Text Classification in the Financial Domain","authors":"Yusuf Arslan, Kevin Allix, Lisa Veiber, Cedric Lothritz, Tegawendé F. Bissyandé, Jacques Klein, A. Goujon","doi":"10.1145/3442442.3451375","DOIUrl":"https://doi.org/10.1145/3442442.3451375","url":null,"abstract":"Neural networks for language modeling have been proven effective on several sub-tasks of natural language processing. Training deep language models, however, is time-consuming and computationally intensive. Pre-trained language models such as BERT are thus appealing since (1) they yielded state-of-the-art performance, and (2) they offload practitioners from the burden of preparing the adequate resources (time, hardware, and data) to train models. Nevertheless, because pre-trained models are generic, they may underperform on specific domains. In this study, we investigate the case of multi-class text classification, a task that is relatively less studied in the literature evaluating pre-trained language models. Our work is further placed under the industrial settings of the financial domain. We thus leverage generic benchmark datasets from the literature and two proprietary datasets from our partners in the financial technological industry. After highlighting a challenge for generic pre-trained models (BERT, DistilBERT, RoBERTa, XLNet, XLM) to classify a portion of the financial document dataset, we investigate the intuition that a specialized pre-trained model for financial documents, such as FinBERT, should be leveraged. Nevertheless, our experiments show that the FinBERT model, even with an adapted vocabulary, does not lead to improvements compared to the generic BERT models.","PeriodicalId":129420,"journal":{"name":"Companion Proceedings of the Web Conference 2021","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130588282","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: 26

Challenges and Opportunities in Using Data Science for Homelessness Service Provision 利用数据科学为无家可归者提供服务的挑战和机遇

Companion Proceedings of the Web Conference 2021 Pub Date : 2021-04-19 DOI: 10.1145/3442442.3453454

C. Chelmis, Wenting Qi, Wonhyung Lee

{"title":"Challenges and Opportunities in Using Data Science for Homelessness Service Provision","authors":"C. Chelmis, Wenting Qi, Wonhyung Lee","doi":"10.1145/3442442.3453454","DOIUrl":"https://doi.org/10.1145/3442442.3453454","url":null,"abstract":"Homelessness service provision, a task of great societal relevance, requires solutions to several urgent problems facing our humanity. Data science, that has recently emerged as a potential catalyst in addressing long standing problems related to human services, offers immense potential. However, homelessness service provision presents unignorable challenges (e.g., assessment methods and data bias) that are are seldom found in other domains, requiring cross-discipline collaborations and cross-pollination of ideas. This work summarizes the challenges offered by homelessness service provision tasks, as well as the problems and the opportunities that exist for advancing both data science and human services. We begin by highlighting typical goals of homelessness service provision, and subsequently describe homelessness service data along with their properties, that make it challenging to use traditional data science methods. Along the way, we discuss some of the existing efforts and promising directions for data science, and conclude by discussing the importance of a deep collaboration between data science and domain experts for synergistic advancements in both disciplines.","PeriodicalId":129420,"journal":{"name":"Companion Proceedings of the Web Conference 2021","volume":"66 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121530354","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: 4

Learning to Persist: Exploring the Tradeoff Between Model Optimization and Experience Consistency 学习坚持:探索模型优化和经验一致性之间的权衡

Companion Proceedings of the Web Conference 2021 Pub Date : 2021-04-19 DOI: 10.1145/3442442.3452051

Dmitri Goldenberg, Guy Tsype, Igor Spivak, Javier Albert, Amir Tzur

引用次数: 6

A Feature Analysis Tool for Batch RL Datasets 批量RL数据集的特征分析工具

Companion Proceedings of the Web Conference 2021 Pub Date : 2021-04-19 DOI: 10.1145/3442442.3453147

Ruiyang Xu, Zhengxing Chen

引用次数: 0

Demonstration of Faceted Search on Scholarly Knowledge Graphs 学术知识图的分面搜索演示

Companion Proceedings of the Web Conference 2021 Pub Date : 2021-04-19 DOI: 10.1145/3442442.3458605

Golsa Heidari, Ahmad Ramadan, M. Stocker, S. Auer

引用次数: 3