Characteristics of Fake News and Misinformation in Greece: The Rise of New Crowdsourcing-Based Journalistic Fact-Checking Models
Abstract
:1. Introduction
2. Literature Review
2.1. Fake News, a World Menace
2.2. Crowdsourcing
- Crowdfunding (fund raising)
- Crowdcreation (the crowd creates)
- Crowdvoting (collective vote)
- Crowdwisdom (collective intelligence)
- A precipitous drop in newspaper circulation numbers and advertising revenues (both classified and print), that has been accentuated by economic downturn since the global financial crisis of 2008;
- A dramatic fall in share prices for commercial media businesses, many of which acquired high levels of debt in the 2000s, and which appear to be struggling to develop new business models for the internet economy;
- A shift in the “attention economy” of media users, who deal with media proliferation by seeking multi-media combinations, and spending less time consuming any single media product or service
- A crisis of authority for professional journalism arising from the shift from the ‘high modernist’ era of crusading investigative journalism and one-off features towards the 24-h news cycle and the need to continuously reproduce news around familiar themes and formats;
- A growing public distrust of journalists who are increasingly being seen as the conduits for material provided to them by well-funded political, business and other special interests (Flew 2009).
2.3. Fact-Checking in Journalism
2.4. Fake News in Greek Mediascape
2.5. Major Social Networks & “Ellinika Hoaxes” Fact-Checking Platform
- (a)
- Dissemination—a post on Facebook with hundreds of notifications or a news item that has been published from a wide range of pages (especially if some are widely considered credible) is considered more urgent than a post on a relatively unknown blog.
- (b)
- The severity of the effects of misinformation: an article with potentially inaccurate medical claims, especially if it can lead patients to suspend their approved treatments, is considered more urgent than an article about a humorous joke.
- Step 1: Potentially suspicious material is identified.
- Step 2: Content analysis: The fact-checking team contacts initial sources.
- Step 3: Audiovisual research: To make sure whether audiovisual material is really related to the article’s allegations.
- Step 4: Examination of scientific studies in cases of pseudo-scientific claims.
- Step 5: Communication with other fact-checking groups/organizations.
3. Methodology & Analysis
3.1. Quantitative Part of the Study
3.2. Qualitative Part of the Study
- Q1.
- Do you consider that there are cases of non-true stories in the Greek public sphere and to what extent? In which categories of news websites do you mainly find fake news?
- Q2.
- Are the major popular news sites “clean” and safe from fake news?
- Q3.
- What is your opinion on Fact-Checking?
- Q4.
- Can the public (crowd) contribute to the fight against fake news? Do you think that a crowdsourcing model (public participation) can help?
- Q5.
- What else do you think could be done to tackle the phenomenon of fake news?
3.2.1. Conducting of the Interviews
3.2.2. Answers
4. Analysis & Discussion
5. Conclusions
6. Limitations
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Non-True Story Type | Cases N | Percentage % |
---|---|---|
misinformation | 137 | 26% |
hoaxes | 201 | 38% |
fake news | 94 | 18% |
conspiracy theories | 12 | 2% |
pseudoscience | 26 | 5% |
scams | 13 | 2% |
mixing of facts and falsifications | 15 | 3% |
myths & history | 15 | 3% |
no | 10 | 2% |
Ν = 521 | Detected in Alexa Top 500 Rankings |
---|---|
N | 217 |
percentage % | 41% |
News Website Category | Cases Detected | Percentage % |
---|---|---|
portals/blogs | 414 | 78% |
tv | 12 | 2% |
newspaper | 34 | 6% |
40 | 8% |
Alexa Top 500 Non-True Stories Cases | Cases Detected | Percentage % |
---|---|---|
Group D < 250 | 166 | 76% |
Group C < 100 | 130 | 60% |
Group B < 50 | 118 | 54% |
Group A < 25 | 89 | 41% |
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Lamprou, E.; Antonopoulos, N.; Anomeritou, I.; Apostolou, C. Characteristics of Fake News and Misinformation in Greece: The Rise of New Crowdsourcing-Based Journalistic Fact-Checking Models. Journal. Media 2021, 2, 417-439. https://doi.org/10.3390/journalmedia2030025
Lamprou E, Antonopoulos N, Anomeritou I, Apostolou C. Characteristics of Fake News and Misinformation in Greece: The Rise of New Crowdsourcing-Based Journalistic Fact-Checking Models. Journalism and Media. 2021; 2(3):417-439. https://doi.org/10.3390/journalmedia2030025
Chicago/Turabian StyleLamprou, Evangelos, Nikos Antonopoulos, Iouliani Anomeritou, and Chrysoula Apostolou. 2021. "Characteristics of Fake News and Misinformation in Greece: The Rise of New Crowdsourcing-Based Journalistic Fact-Checking Models" Journalism and Media 2, no. 3: 417-439. https://doi.org/10.3390/journalmedia2030025
APA StyleLamprou, E., Antonopoulos, N., Anomeritou, I., & Apostolou, C. (2021). Characteristics of Fake News and Misinformation in Greece: The Rise of New Crowdsourcing-Based Journalistic Fact-Checking Models. Journalism and Media, 2(3), 417-439. https://doi.org/10.3390/journalmedia2030025