# Mimicking Human Verification Behavior for News Media Credibility Evaluation
## 6 Conclusions  
This work is inspired by the human user's factual verification behavior in media credibility evaluation, and it proposes a novel new media credibility evaluation method called the MiBeMC. Specifically, we refined media credibility assessment into a multi factor module fusion problem, aiming to extract features within and between modules. Firstly, we designed a feature extractor intra module in order to simulate the semantic understanding process of each news element. Then, we constructed an association similarity module to simulate the credibility of the same media's news that humans have previously seen when they follow the release of the news media as well as other title and content verification behaviors of readers searching related topics. Simultaneously, these behaviors are integrated into a sequence through the aggregation module in order to mimic the entire human verification process. Finally, a media credibility detector was constructed to predict the credibility of the news outlet. In addition, regularized adversarial training can alleviate redundant information in the dataset, effectively avoiding overfitting that is caused by learning information unrelated to credibility labels, and thereby enhancing the model's generalization ability. The experiment has demonstrated the effectiveness of MiBeMC in the media credibility evaluation task.  
However, our proposed model has some limitations. Firstly, in data truncation ablation experiments, we found the impact of data truncation on model performance, but we have not yet conducted in-depth research on how to accurately control the truncation range in order to maximize its effectiveness. Secondly, it currently does not support languages other than English, and due to the lack of relevant datasets, we are unable to further explore the role of multimodal data in the media credibility evaluation task. In future work, we will conduct in-depth analysis of the role of precise data truncation technology in media credibility assessment tasks. In order to fill the multimodal data gaps in this task, we will also attempt to construct a multimodal media credibility evaluation dataset, further exploring the role of media credibility evaluation in the online media environment, and hoping to provide some technical support for promoting the sustainable development of the environment of news outlets.  
Conceptualization, W.F. and Y.W.; methodology, W.F.; software, W.F.; validation, W.F.; formal analysis, W.F.; investigation, W.F.; resources, W.F.; data curation, W.F. and H.H.; writing--original draft preparation, W.F.; writing--review and editing, W.F. and Y.W.; visualization, W.F. and H.H.; supervision, Y.W.; project administration, Y.W.; funding acquisition, Y.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Key Research and Development Program of China, grant number 2021 YFF0901602.  
Not applicable.  
In the work, no new data were created. The dataset used in this work can be accessed on [[https://gitlab.com/checkthat_lab/clef2023-checkthat-lab/-/tree/main/task4](https://gitlab.com/checkthat_lab/clef2023-checkthat-lab/-/tree/main/task4)]([https://gitlab.com/checkthat_lab/clef2023-checkthat-lab/-/tree/main/task4](https://gitlab.com/checkthat_lab/clef2023-checkthat-lab/-/tree/main/task4)) (accessed on 2 June 2023).  
The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Aggregation Order** & **MAE** \\ \hline T-C, T-Hs, C-Hs, C-Ss, Hs-Ss & 0.273 \\ T-C, C-Ss, Hs-Ss, T-Hs, C-Hs & 0.320 \\ C-Hs, C-Ss, T-C, T-Hs, Hs-Ss & 0.328 \\ C-Ss, Hs-Ss, T-C, T-Hs, C-Hs & 0.336 \\ Hs-Ss, T-Hs, C-Hs, C-S, T-C & 0.359 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Performance of different aggregation order on the CLEF-2023 Task 4 dataset.