Posting a sticky comment encouraging skepticism caused a comment to be 1.28 percentage points more likely to include at least one link.
#Reddit world news plus#
Subreddit moderators produced a list of news sources “that frequently receive complaints” about “sensationalized headlines and unreliable information.” (Mostly British and Australian tabloid newspapers, interestingly, plus the New York Post.) Links to those sources submitted to the subreddit over a two-month period were either: left alone appended with a comment encouraging skepticism and a fact-check or appended with a comment that encouraged both fact-checking and downvoting “if you can’t independently verify these claims.” The findings:
“Our results here showed that many of the issues we care about, such as the spread of fake news, are shaped by a combination of human and algorithmic factors, and that we can influence algorithms by persuading people to shift their behavior, even if we don’t control those algorithmic systems,” Matias said. (The idea is borrowed from the research of Richard Thaler and Cass Sunstein, who detailed how nudges could be used in government and other institutions.) Nathan Matias, a Ph.D candidate and the experiment’s lead researcher, said the experiment proved the power of what he calls the “AI nudge,” which combines human persuasion and algorithms to generate a desired effect, while not imposing any actual limitations on user behavior. In a recent collaboration with Reddit’s /r/worldnews community, researchers at MIT found that encouraging users to fact-check potentially misleading or sensationalist stories both doubled the number of comments with links that those posts got and halved their Reddit scores (pushing them farther down the page), “a statistically significant effect that likely influenced rankings in the subreddit,” says the report. Also, improvements to the processing capability of the server and efficiency in database queries can increase the scope of data collection.To curb the spread of unreliable news, should news organizations and platforms turn to algorithms or rely on users themselves? One recent experiment suggests a potential solution could combine the two. Future improvements to the project could include an increase in accuracy through advancements in analysis techniques and exploration of different analysis types. The implementation of the project was done using Python libraries and frameworks. Data collection was done hourly on all articles from the world news homepage of BBC and Reuters News, as well as the top 30 submissions on r/worldnews, including the top 50 comments of each submission. These analytical information will be displayed in a simple web interface to aid users and researchers in understanding the viewpoints of the community as well as identify news that are not reported widely. Using a combination of existing web design and analysis tools, the project aims to build a web application which collects and analyses responses from r/worldnews and news articles from BBC and Reuters News. Existing analysis methods including topic, time, keyword, summary, sentiment and comparison analysis as well as frameworks were researched and improved upon to provide the ideal effectiveness and efficiency for this project. Related works had mainly focussed on social media responses, but not on digital news sources. To overcome this issue, a combination of social media responses and digital news sources can be utilised for analysis. However, digital news from news publications may be biased and inaccurate due to censorships or other agendas. News and social media consumption form a large part of the technology revolution. In the current day and age of modernised society, technology has become an integral part of life. Web application for analysis of world news on Reddit and news publicationsĮngineering::Computer science and engineering::Computing methodologies::Artificial intelligence
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