1
From reading the paper “Unmasking the Conversation on Masks: NLP for Topical Sentiment Analysis of COVID-19 Twitter Discourse” it was hard to define certainly, which of the approaches mentioned in the Marres and Moats paper were applied to the current research. The easier way is to say, which approaches were not applied and to confirm this position. Firstly, according to the paper “Tampering with Twitter’s Sample API” by Pfeffer, Mayer, and Morstatter, Twitter API provides researchers with unrepresentative data sampling, which obviously can easily lead to wrong results of an analysis, and it can be treated as the feature of the mentioned API and Twitter itself. There were no mentions of this feature in the current research and these characteristics were not taken into account. That’s being said, we can’t say for sure that the researchers used a truly precautionary approach or, at least, to try to use such an approach was not perfect.
2
The model shows good results in finding similar comments. However, it has some problems with the heat map, as the model fails to plot the similarities in this way. I can imagine using this model to find similar comments and to analyze them. Since our goal is to detect bots, it may be possible to find the bot’s comment and to find similar comments using the model. It could limit human interaction, as it would be sufficient to find only 1 bot-comment using a human. Then, the process could be repeated iteratively and all the similar comments could be treated as the bot’s comments. There can be another way to use the model. It may be not that easy to say for sure that a comment was written by a bot. To prove the assumption the model can be used: if there are a lot of super (open question: how to define the border?) similar comments - they could be written by the bots.
1 From reading the paper “Unmasking the Conversation on Masks: NLP for Topical Sentiment Analysis of COVID-19 Twitter Discourse” it was hard to define certainly, which of the approaches mentioned in the Marres and Moats paper were applied to the current research. The easier way is to say, which approaches were not applied and to confirm this position. Firstly, according to the paper “Tampering with Twitter’s Sample API” by Pfeffer, Mayer, and Morstatter, Twitter API provides researchers with unrepresentative data sampling, which obviously can easily lead to wrong results of an analysis, and it can be treated as the feature of the mentioned API and Twitter itself. There were no mentions of this feature in the current research and these characteristics were not taken into account. That’s being said, we can’t say for sure that the researchers used a truly precautionary approach or, at least, to try to use such an approach was not perfect.
2 The model shows good results in finding similar comments. However, it has some problems with the heat map, as the model fails to plot the similarities in this way. I can imagine using this model to find similar comments and to analyze them. Since our goal is to detect bots, it may be possible to find the bot’s comment and to find similar comments using the model. It could limit human interaction, as it would be sufficient to find only 1 bot-comment using a human. Then, the process could be repeated iteratively and all the similar comments could be treated as the bot’s comments. There can be another way to use the model. It may be not that easy to say for sure that a comment was written by a bot. To prove the assumption the model can be used: if there are a lot of super (open question: how to define the border?) similar comments - they could be written by the bots.