Novelty in News Search: A Longitudinal Study of the 2020 US Elections

IF 3 2区社会学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Social Science Computer Review Pub Date : 2023-08-14 DOI:10.1177/08944393231195471

Roberto Ulloa, Mykola Makhortykh, Aleksandra Urman, Juhi Kulshrestha

{"title":"Novelty in News Search: A Longitudinal Study of the 2020 US Elections","authors":"Roberto Ulloa, Mykola Makhortykh, Aleksandra Urman, Juhi Kulshrestha","doi":"10.1177/08944393231195471","DOIUrl":null,"url":null,"abstract":"The 2020 US elections news coverage was extensive, with new pieces of information generated rapidly. This evolving scenario presented an opportunity to study the performance of search engines in a context in which they had to quickly process information as it was published. We analyze novelty, a measurement of new items that emerge in the top news search results, to compare the coverage and visibility of different topics. Using virtual agents that simulate human web browsing behavior to collect search engine result pages, we conduct a longitudinal study of news results of five search engines collected in short bursts (every 21 minutes) from two regions (Oregon, US and Frankfurt, Germany), starting on election day and lasting until one day after the announcement of Biden as the winner. We find more new items emerging for election related queries (“joe biden,” “donald trump,” and “us elections”) compared to topical (e.g., “coronavirus”) or stable (e.g., “holocaust”) queries. We demonstrate that our method captures sudden changes in highly covered news topics as well as multiple differences across search engines and regions over time. We highlight novelty imbalances between candidate queries which affect their visibility during electoral periods, and conclude that, when it comes to news, search engines are responsible for such imbalances, either due to their algorithms or the set of news sources that they rely on.","PeriodicalId":49509,"journal":{"name":"Social Science Computer Review","volume":"34 1","pages":"0"},"PeriodicalIF":3.0000,"publicationDate":"2023-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Social Science Computer Review","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1177/08944393231195471","RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}

引用次数: 0

Abstract

The 2020 US elections news coverage was extensive, with new pieces of information generated rapidly. This evolving scenario presented an opportunity to study the performance of search engines in a context in which they had to quickly process information as it was published. We analyze novelty, a measurement of new items that emerge in the top news search results, to compare the coverage and visibility of different topics. Using virtual agents that simulate human web browsing behavior to collect search engine result pages, we conduct a longitudinal study of news results of five search engines collected in short bursts (every 21 minutes) from two regions (Oregon, US and Frankfurt, Germany), starting on election day and lasting until one day after the announcement of Biden as the winner. We find more new items emerging for election related queries (“joe biden,” “donald trump,” and “us elections”) compared to topical (e.g., “coronavirus”) or stable (e.g., “holocaust”) queries. We demonstrate that our method captures sudden changes in highly covered news topics as well as multiple differences across search engines and regions over time. We highlight novelty imbalances between candidate queries which affect their visibility during electoral periods, and conclude that, when it comes to news, search engines are responsible for such imbalances, either due to their algorithms or the set of news sources that they rely on.

查看原文

微信好友朋友圈 QQ好友复制链接

本刊更多论文

新闻搜索中的新颖性:对2020年美国大选的纵向研究

2020年美国大选的新闻报道非常广泛，新信息层出不穷。这个不断发展的场景提供了一个研究搜索引擎在发布信息时必须快速处理的环境中的性能的机会。我们分析新颖性，即在新闻搜索结果中出现的新项目的衡量标准，以比较不同主题的覆盖率和可见性。使用模拟人类网络浏览行为的虚拟代理来收集搜索引擎结果页面，我们对从两个地区(美国俄勒冈州和德国法兰克福)以短时间(每21分钟)收集的五个搜索引擎的新闻结果进行了纵向研究，从选举日开始，一直持续到宣布拜登获胜后的一天。我们发现，与选举相关的查询(“乔·拜登”、“唐纳德·特朗普”和“美国选举”)相比，热门查询(如“冠状病毒”)或稳定查询(如“大屠杀”)出现了更多的新条目。我们证明，我们的方法可以捕捉到新闻主题的突然变化，以及搜索引擎和地区之间随时间的多种差异。我们强调了候选人查询之间的新颖性不平衡，这影响了他们在选举期间的可见性，并得出结论，当涉及到新闻时，搜索引擎要对这种不平衡负责，要么是由于他们的算法，要么是他们所依赖的新闻来源。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文去求助

来源期刊

Social Science Computer Review 社会科学-计算机：跨学科应用

CiteScore

9.00

自引率

4.90%

发文量

审稿时长

>12 weeks

期刊介绍： Unique Scope Social Science Computer Review is an interdisciplinary journal covering social science instructional and research applications of computing, as well as societal impacts of informational technology. Topics included: artificial intelligence, business, computational social science theory, computer-assisted survey research, computer-based qualitative analysis, computer simulation, economic modeling, electronic modeling, electronic publishing, geographic information systems, instrumentation and research tools, public administration, social impacts of computing and telecommunications, software evaluation, world-wide web resources for social scientists. Interdisciplinary Nature Because the Uses and impacts of computing are interdisciplinary, so is Social Science Computer Review. The journal is of direct relevance to scholars and scientists in a wide variety of disciplines. In its pages you''ll find work in the following areas: sociology, anthropology, political science, economics, psychology, computer literacy, computer applications, and methodology.