{"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
{"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}