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Table 5. Coefficients of model quality for media trust and fact-checking source.

Table 5 Coefficients of model quality for media trust and fact-checking source.

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Table 2. Factor loadings for fact checker roles.
Table 2. Factor loadings for fact checker roles.
Table 3. Agreement (rather & strongly), disagreement (rather & strongly) for fact checker roles wit! mean (five-point scale) and standard deviation.
Table 3. Agreement (rather & strongly), disagreement (rather & strongly) for fact checker roles wit! mean (five-point scale) and standard deviation.
Figure 1. Perceived credibility of fact-checking sources in percent. Note: Mean values >3 are con- sidered credible and values <3 not credible. fact-checking sources and messages. Coefficients for model quality can be found in Table 4. However, hypothesis 2 can only be partially supported by the results of the present study. As shown in Table 5, political ideology is no longer significant in a more complex model with trust in media and politics. CMTTINICA TIPO VVELEE CPUot TEE TEP Ua GI PUTIN. The first aim of this study was to find out to what extent the perceived credibility of fake news and fact-checking could be explained by trust in media and politics. In this sample, overall media trust was high (MV = 3.59, SD= 1.15). Trust in politics was equally strong (M = 3.36, SD= 1.14) as was trust in science (M=4.47, SD=.74) and conventiona medicine (M = 4.01, SD = .79). However, a considerably high number (23.6%) of the partici pants leaned towards distrust in media and politics. Very few people tended to trus alternative media (V=1.96, SD=1.05). Approximately one third of the participants each trusted and distrusted fact-checking websites (MV = 2.99, SD= 1.15). All trust scores are displayed in Figure 2. The first aim of this study was to find out to what extent the perceived credibility of
Figure 1. Perceived credibility of fact-checking sources in percent. Note: Mean values >3 are con- sidered credible and values <3 not credible. fact-checking sources and messages. Coefficients for model quality can be found in Table 4. However, hypothesis 2 can only be partially supported by the results of the present study. As shown in Table 5, political ideology is no longer significant in a more complex model with trust in media and politics. CMTTINICA TIPO VVELEE CPUot TEE TEP Ua GI PUTIN. The first aim of this study was to find out to what extent the perceived credibility of fake news and fact-checking could be explained by trust in media and politics. In this sample, overall media trust was high (MV = 3.59, SD= 1.15). Trust in politics was equally strong (M = 3.36, SD= 1.14) as was trust in science (M=4.47, SD=.74) and conventiona medicine (M = 4.01, SD = .79). However, a considerably high number (23.6%) of the partici pants leaned towards distrust in media and politics. Very few people tended to trus alternative media (V=1.96, SD=1.05). Approximately one third of the participants each trusted and distrusted fact-checking websites (MV = 2.99, SD= 1.15). All trust scores are displayed in Figure 2. The first aim of this study was to find out to what extent the perceived credibility of
Table 4. Coefficients of model quality for political ideology.
Table 4. Coefficients of model quality for political ideology.
HHGIILY Were HIRIMUUEU ado CUIILIUTS. For fact-checking source credibility the model was found to be significant, F(10, 110) = 11.94, p< .001, R? =.52. Only trust in media ventional medicine (6 = .25, p= .004) were cal ideology was not significant (6 = —.14, and politics (6 = .37, p < .001) and trust in con- found to be significant predictors while politi- p=.061). Because the perceived credibility of Facebook community as a fact-checking source was that much lower than for dpa and CORRECTIV (see Figure 1), the same mod el was calculated again without the fictious fact-checking source. The model was found to be significant, F(10, 73) = 9.81, p< .001, R*=.57. This time, additionally the col aborative-facilitator (8 =—.27, p=.015) was found to be a significant predictor which indicates that it is more important for the per- ceived credibility of professional fact checkers than for laypersons. Coefficients of model quality can be found in Table 5.
HHGIILY Were HIRIMUUEU ado CUIILIUTS. For fact-checking source credibility the model was found to be significant, F(10, 110) = 11.94, p< .001, R? =.52. Only trust in media ventional medicine (6 = .25, p= .004) were cal ideology was not significant (6 = —.14, and politics (6 = .37, p < .001) and trust in con- found to be significant predictors while politi- p=.061). Because the perceived credibility of Facebook community as a fact-checking source was that much lower than for dpa and CORRECTIV (see Figure 1), the same mod el was calculated again without the fictious fact-checking source. The model was found to be significant, F(10, 73) = 9.81, p< .001, R*=.57. This time, additionally the col aborative-facilitator (8 =—.27, p=.015) was found to be a significant predictor which indicates that it is more important for the per- ceived credibility of professional fact checkers than for laypersons. Coefficients of model quality can be found in Table 5.
Table 6. Coefficients of model quality for media trust and fact-checking message. to be significant. As a comparison, for real news credibility, the model was found to be significant, F(10, 110) = 3.58, p< .001, R?=.25 with only trust in conventional medicine as a significant predictor (6 =.27, p=.013). However, it needs to be pointed out again here that the real news fillers used did not include source cues for credibility as opposed to the fact checks and a comparison of both was not the aim of this study. Coeffi- cients of model quality can be found in Table 7. WO eis eae ee bee abe ete ee Da ee eee oe ie ee ce i i ce I gy ee ee ee sec al
Table 6. Coefficients of model quality for media trust and fact-checking message. to be significant. As a comparison, for real news credibility, the model was found to be significant, F(10, 110) = 3.58, p< .001, R?=.25 with only trust in conventional medicine as a significant predictor (6 =.27, p=.013). However, it needs to be pointed out again here that the real news fillers used did not include source cues for credibility as opposed to the fact checks and a comparison of both was not the aim of this study. Coeffi- cients of model quality can be found in Table 7. WO eis eae ee bee abe ete ee Da ee eee oe ie ee ce i i ce I gy ee ee ee sec al
Figure 3. Percentage of perceived fact-checking and disinformation credibility as a function of media trust.
Figure 3. Percentage of perceived fact-checking and disinformation credibility as a function of media trust.
*p <.05; **p < .01; ***p < .001. Note: different format! No source was given for news posts, functioned only as fillers! Table 7. Coefficients of model quality for media trust and fake news with comparison real news posts.
*p <.05; **p < .01; ***p < .001. Note: different format! No source was given for news posts, functioned only as fillers! Table 7. Coefficients of model quality for media trust and fake news with comparison real news posts.
Related topics:
JournalismFunctions and Roles of JournalismCombating Misinformation Online
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