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Academic literature lacks agreement on which individual factors contribute to the difficulty in detecting fake news and the tendency to spread it. Regarding detection, Allcott and Gentzkow4 found that partisan individuals have more difficulty identifying fake news that confirms their political stances. After the 2016 US presidential election, partisan individuals were more likely to believe headlines that favored their preferred candidate. This ideological bias was substantially more robust for those who relied on ideology-based social media networks. Bail et al.8 conducted a field experiment that offered a large group of Democrats and Republicans financial compensation to follow bots that retweeted messages by elected officials and opinion leaders with opposing political views. Surprisingly, Republican participants expressed substantially more conservative views after following a liberal Twitter bot. Pennycook and Rand9 concluded that it is primarily analytical thinking that matters for assessing of news headlines independent of whether a headline matches one's political ideology. In the United Kingdom, Preston et al.10 found that higher levels of educational attainment and emotional intelligence go along with a better ability to detect fake news. However, there is still a lack of systematic evidence on the effect of age and other individual-level characteristics such as education, gender, or income on the ability to detect fake news. Such evidence may help target particularly vulnerable groups with appropriate policy measures such as fake news alerts that remind viewers of potentially misleading or false information. Moreover, most previous studies did not differentiate whether fake news is shared deliberately or accidentally. The distinction is crucial when it comes to policy conclusions. If fake news is spread mostly accidentally, then the policy can focus on citizens' ability to identify fake news. If fake news is mostly shared deliberately, improving identification will not help; a broader approach to overcoming the societal divide may be needed. The headlines were presented as a text and not combined with any visual material to avoid deception, e.g., falsely suggesting the appearance in specific (social) media outlets. We asked respondents to assess these headlines and created an index that quantifies their ability to detect fake news (for details, see "Methods" Section). We also asked respondents how likely they would spread each of the ten news headlines. By comparing the identification of fake news items with the intention to share them, we can distinguish accidental and deliberate sharing. We hypothesize that most sharing is due to a lack of detection, i.e., accidental sharing dominates. Recent research has shown that most people are overconfident in their own ability to spot fake news25. The overconfidence in one's own ability to distinguish true and false information may contribute to spreading fake news26. Differences in overconfidence may explain why some groups fall more easily for fake news. As overconfidence is generally higher among men27, particularly in tasks with low-performance feedback28 such as news sharing, accidental sharing should be higher among male respondents. We also expect more accidental sharing among the younger individuals29. Data collection 5. To measure whether respondents share fake news, we asked "Would you consider sharing each of the following stories online (for example, through Facebook or Twitter)?", after which we provided the same 5 fake news headlines and 5 true headlines outlined above. The respondents could choose between 'no', 'maybe', and 'yes'. We then constructed three measures of fake news sharing. First, general fake news sharing was constructed as a scale ranging from 0 to 10 with a higher value indicating a higher propensity to share. To construct this scale, we considered the five fake news headlines and gave a value of 2 if the respondent answered 'yes', 1 for 'maybe', and 0 for 'no'. We then aimed to distinguish between the deliberate and accidental sharing of fake news by constructing two additional scales. The first scale is deliberate sharing, meaning that the respondent recognized the fake news headlines were fake but wanted to share them anyway. Respondents recognized that a fake news headline was fake if they answered 'extremely unlikely' and 'somewhat unlikely' on the question "To the best of your knowledge, how likely is it that the claim in each of the headlines is correct?". We consider that respondents wanted to share the fake news headlines if they answered 'yes' or 'maybe' to the question "Would you consider sharing each of the following stories online (for example, through Facebook or Twitter)?". We then counted the number of headlines respondents were willing to share deliberately and constructed a scale ranging from 0 to 5. The second scale is accidental sharing meaning that the respondents did not recognize the fake news headlines were fake and wanted to share them. Analogous questions were used for deliberate sharing, except that not recognizing was defined as answering 'neither likely nor unlikely', 'somewhat likely', and 'extremely likely' on the detection of fake news questions. This scale was also constructed by counting the number of headlines respondents were willing to share accidentally, ranging from 0 to 5. We estimate how various fake news measures (fake news detection, general fake news sharing, deliberate fake news sharing, and accidental fake news sharing) relate to different individual-level characteristics using a linear model estimated by Ordinary Least Squares (OLS): $$\begin{aligned} Y_{i} = \alpha + \beta \mathbf {S_{i}} + \epsilon _{i} \end{aligned}$$ replica bags
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