Even in a world where people increasingly get news from social media, the professional news media is still seen as largely to blame for low trust according to a new report from the Reuters Institute for the Study of Journalism, which examines the underlying reasons for trust and distrust in the news media (and in social media) across nine countries. Bias, spin and hidden agendas come across as the main reasons for lack of trust in the news media along with a perceived decline in journalistic standards driven by greater competition and some online business models. These concerns are strongest with the young and with those on low incomes. Trust in the news that people find in social media is lower still, but similar trends are at play - bias, agendas and low quality information. The report argues that this is largely a function of a model that allows anybody to publish without checks, and algorithms that sometimes favour extreme or contentious content. The study is based on analysing thousands of open-ended responses from the 2017 Reuters Institute Digital News Report, where respondents were asked to give their reasons for low trust in their own words, using open-ended text fields. By coding and analysing responses, the report categorises the specific issues that are driving public concern across countries as well as those that build trust such as journalistic processes, strong brands and quality journalism delivered over time.
路透社新闻研究所(Reuters Institute for the Study of Journalism)的一份新报告显示，即使在一个人们越来越多地从社交媒体上获取新闻的世界里，专业新闻媒体仍被视为信任度低的主要原因。该报告调查了9个国家对新闻媒体(以及社交媒体)的信任和不信任的根本原因。偏见、捏造和隐藏的议程是人们对新闻媒体缺乏信任的主要原因，此外，竞争加剧和一些在线商业模式推动了人们对新闻标准的感知下降。这些担忧在年轻人和低收入人群中表现得最为强烈。人们对社交媒体上新闻的信任度更低，但类似的趋势也在起作用——偏见、议程和低质量的信息。报告认为，这在很大程度上是一种模式的作用，这种模式允许任何人在没有检查的情况下发表文章，而算法有时倾向于极端或有争议的内容。这项研究是基于对2017年路透社研究所数字新闻报告中数千份开放式回复的分析，受访者被要求使用开放式文本字段给出他们对自己的话信任度低的原因。通过对回应进行编码和分析，该报告对引起各国公众关注的具体问题以及建立信任的问题进行了分类，如新闻流程、强大的品牌和长期提供的高质量新闻。
{"title":"Bias, Bullshit and Lies: Audience Perspectives on Low Trust in the Media","authors":"N. Newman, R. Fletcher","doi":"10.2139/ssrn.3173579","DOIUrl":"https://doi.org/10.2139/ssrn.3173579","url":null,"abstract":"Even in a world where people increasingly get news from social media, the professional news media is still seen as largely to blame for low trust according to a new report from the Reuters Institute for the Study of Journalism, which examines the underlying reasons for trust and distrust in the news media (and in social media) across nine countries. \u0000Bias, spin and hidden agendas come across as the main reasons for lack of trust in the news media along with a perceived decline in journalistic standards driven by greater competition and some online business models. These concerns are strongest with the young and with those on low incomes. Trust in the news that people find in social media is lower still, but similar trends are at play - bias, agendas and low quality information. The report argues that this is largely a function of a model that allows anybody to publish without checks, and algorithms that sometimes favour extreme or contentious content. \u0000The study is based on analysing thousands of open-ended responses from the 2017 Reuters Institute Digital News Report, where respondents were asked to give their reasons for low trust in their own words, using open-ended text fields. \u0000By coding and analysing responses, the report categorises the specific issues that are driving public concern across countries as well as those that build trust such as journalistic processes, strong brands and quality journalism delivered over time.","PeriodicalId":404371,"journal":{"name":"CommRN: Public Opinion (Topic)","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122421241","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}
A. Tumasjan, T. Sprenger, Philipp G. Sandner, I. Welpe
This study investigates whether microblogging messages on Twitter validly mirror the political landscape offline and can be used to predict election results. In the context of the 2009 German federal election, we conducted a sentiment analysis of over 100,000 messages containing a reference to either a political party or a politician. Our results show that Twitter is used extensively for political deliberation and that the mere number of party mentions accurately reflects the election result. The tweets' sentiment (e.g., positive and negative emotions associated with a politician) corresponds closely to voters' political preferences. In addition, party sentiment profiles reflect the similarity of political positions between parties. We derive suggestions for further research and discuss the use of microblogging services to aggregate dispersed information.
{"title":"Election Forecasts with Twitter - How 140 Characters Reflect the Political Landscape","authors":"A. Tumasjan, T. Sprenger, Philipp G. Sandner, I. Welpe","doi":"10.2139/ssrn.1833192","DOIUrl":"https://doi.org/10.2139/ssrn.1833192","url":null,"abstract":"This study investigates whether microblogging messages on Twitter validly mirror the political landscape offline and can be used to predict election results. In the context of the 2009 German federal election, we conducted a sentiment analysis of over 100,000 messages containing a reference to either a political party or a politician. Our results show that Twitter is used extensively for political deliberation and that the mere number of party mentions accurately reflects the election result. The tweets' sentiment (e.g., positive and negative emotions associated with a politician) corresponds closely to voters' political preferences. In addition, party sentiment profiles reflect the similarity of political positions between parties. We derive suggestions for further research and discuss the use of microblogging services to aggregate dispersed information.","PeriodicalId":404371,"journal":{"name":"CommRN: Public Opinion (Topic)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130544337","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：