{"title":"Reel or Real – Survey of Machine Learning to Adversarial Machine Learning Algorithms for Fake Content Detection in the Education Domain","authors":"Janice Marian Jockim, M. K, Karthika S","doi":"10.2139/ssrn.3735766","DOIUrl":"https://doi.org/10.2139/ssrn.3735766","url":null,"abstract":"The issue of fake news has developed a lot quicker in the ongoing years. Online media has drastically changed its scope and effect all in all. On one hand, it's easy, and simple availability with a quick portion of data draws more consideration of individuals to peruse news from it. Then again, it empowers widespread of fake news, which are only false data to misdirect individuals. With 14 percent of individuals conceding that they have purposely shared a fake political report on the web, unmistakably these false reports will keep on picking up footing insofar as individuals are as yet ready to share them on the web. Subsequently, computerizing counterfeit news recognition has gotten urgent to keep up strong on the web and web-based media. There have been many machine learning approaches implemented to address and solve this problem. Likewise, this project, given a certain number of instances shared publicly on a social media platform, helps in tracing the instances one by one and identifies the fake news using supervised learning algorithms. It then generates news implying mixed viewpoints by applying adversarial machine learning algorithm and helps in creating a diversion. Thus, it reduces the impact created by misleading information. The prime focus of this paper is to compare the various existing Machine Learning, Deep Learning, Adversarial Machine Learning algorithms which will aid a researcher to understand the fake content detection spreading in the social media which are related to the educational domain. This survey paper will be useful for students during pandemic and crises like the covid-19 pandemic.","PeriodicalId":331367,"journal":{"name":"Journalism Studies eJournal","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116347129","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}
{"title":"Social Media and Newsroom Production Decisions","authors":"Julia Cagé, Nicolas Hervé, B. Mazoyer","doi":"10.2139/ssrn.3663899","DOIUrl":"https://doi.org/10.2139/ssrn.3663899","url":null,"abstract":"Social media affects not only the way we consume news, but also the way news is pro- duced, including by traditional media outlets. In this paper, we study the propagation of information from social media to mainstream media, and investigate whether news ed- itors are influenced in their editorial decisions by stories popularity on social media. To do so, we build a novel dataset including a representative sample of all tweets produced in French between July 2018 and July 2019 (1.8 billion tweets, around 70% of all tweets in French during the period) and the content published online by about 200 mainstream media during the same time period, and develop novel algorithms to identify and link events on social and mainstream media. To isolate the causal impact of popularity, we rely on the structure of the Twitter network and propose a new instrument based on the interaction between measures of user centrality and news pressure at the time of the event. We show that story popularity has a positive effect on media coverage, and that this effect varies depending on media outlets’ characteristics. These findings shed a new light on our understanding of how editors decide on the coverage for stories, and question the welfare effects of social media.","PeriodicalId":331367,"journal":{"name":"Journalism Studies eJournal","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122501734","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}
{"title":"Political Polarization in Financial News","authors":"E. Goldman, Nandini Gupta, Ryan D. Israelsen","doi":"10.2139/ssrn.3537841","DOIUrl":"https://doi.org/10.2139/ssrn.3537841","url":null,"abstract":"Political news is known to be polarized, but standard explanations for polarization do not apply to financial news. Nevertheless, we find strong evidence of political polarization in the tone and coverage of corporate financial news. In particular, we find that the tone of corporate financial news coverage is more positive, and the likelihood that good (bad) news is reported is higher (lower), if the firm is politically aligned with the news source. Such polarization implies that different market participants may be exposed to differing news about the same firm on the same day. Consistent with this argument, we find that disagreement between news sources increases trading volume, and these effects are larger for firms at the political extremes. Our paper highlights a novel source of bias in financial news that can be important for investors.","PeriodicalId":331367,"journal":{"name":"Journalism Studies eJournal","volume":"273 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134275013","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}
{"title":"The Newsroom Dilemma","authors":"Ayush Pant, Federico Trombetta","doi":"10.2139/ssrn.3447908","DOIUrl":"https://doi.org/10.2139/ssrn.3447908","url":null,"abstract":"Conventional wisdom suggests that competition in the modern digital environment is pushing media outlets towards early release of less accurate information. We show that this is not necessarily the case. We argue that two opposing forces determine the resolution of the speed-accuracy tradeoff: preemption and reputation. More competitive environments may be more conducive to reputation building. Therefore, it is possible to have better reporting in a more (Internet-driven) competitive world. However, we show that the audience may be worse-off due to another consequence of the Internet – outlets' better initial information. Finally, we show how a source may exploit the speed-accuracy tradeoff to get \"unverified facts\" out to the audience quickly.","PeriodicalId":331367,"journal":{"name":"Journalism Studies eJournal","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127983497","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}
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
