We have now added a Limitations and Future Research section to the Discussion to address this point and others: There are two limitations in our data curation process, the automated approach we used to check and select papers. Firstly, automatic classifiers of any sort can include some irrelevant or false positive articles. Due to the large amount of articles in our dataset, in Step 2 of our methodology, we developed a machine learning binary classifier – a support vector machine - with 87.56% accuracy to automatically identify relevant articles (accuracy is the number of correct predictions made, divided by the total number of predictions made, and then multiplied by 100 to convert it into a percentage). As a rule of thumb, accuracy of a predictive model that is above 80% is very commonly used to summarise the performance of that model. Still, it doesn’t exclude the possibility of irrelevant articles in our final dataset, but that possibility is less than 12.44 percent and this has to be considered against the benefit of scale and efficiency that this method allows. We also acknowledge that our analysis limited is in not being able to consider visual content visual content, images within articles (known as image framing), which have been shown to carry stigmatising elements [48]. This is something that could be added to the approach by including image classification along with additional measures. Furthermore, even though the Dow Jones is one the largest news databases, it might still miss some articles or news sources (although this doesn’t relate to the automatic approach as such). This applies to social media, even though this would not span as long a timeframe.
You report that an automatic approach was used to check all the papers. Is there any possible limit with this approach in the selection of the papers?