Abstract:

Fake news is considered an increasingly important problem of the current information society and especially the young generation is affected by it. Based on various definitions we can define fake news as intentionally false news.

Exposure to fake news has effects on decision making and problem solving. It causes confusion about what is true, doubt about accurate understandings, subsequent reliance on falsehoods and impacts individual’s mental and social well-being (Rapp and Salovich, 2018). This happens in spite of prior knowledge and experiences. Students as young people are especially vulnerable and sensitive to fake news as they spend a lot of time online and don’t have a stable opinion yet. They also report that they rely more on social networking sites than any other source of information. That is why it is necessary to identify which way of countering fake news works best for students and to integrate fake news countering into education.

In order to avoid the negative consequences it is necessary to check the news. There are two big categories of fake news detection: the manual fact-checking and automatic fact checking.

Manual fact-checking can be done by state institutions, private companies or a non-profit organization. The method relies on experts in the domain who verify the given news content. Its advantage consists in easy manageability, accurate results, but it is costly and has problems processing the increased volumes of data to be checked.

Automatic fact-checking has been developed as a solution to the increased volumes of newly created information. Automatic fact-checking is not without problems. Its problem consists in the knowledge base with which the news are compared. The source of the data in the database must be reliable and not biased.

Another issue is the labelling of the detected fake news. Here four aporoaches were identified (Kirchner and Reuter, 2020): deleting fake news, labelling information as fake news, labelling information with the explanation why the news was labelled and with link to the correct information or a warning sign that requires user’s interaction and so disrupting the automatic interaction with the content.

In our research we focused on university students and their assessment of various fake news detection methods and authorities labelling fake news. We also aimed at fake news tagging and alerting and investigated the third person effect according to which users evaluate and assess themselves and others differently. Asking 85 Czech university students in an online questionnaire we investigated their attitude towards countermeasures against fake news. Our results show students prefer warning labels or warning labels with a link to the correct information to fake news deleting. Although our respondents are quite sure about fake news tagging, they are not so sure about who should be responsible for that. They quite trust nongovernmental institutions in this respect and prefer human tagging to the automatic one. The third person effect was manifest which should be considered in the battle against fake news. To conclude we can say respondents welcome countermeasures against fake news, but don’t prefer fake news deletion to preserve freedom of speech. Special attention should be paid to the entity responsible for news control. Its trustworthiness should be supported.

Keywords:

Fake news, fake news labelling, third person effect, fact checking.