Venue: EMNLP 2020 Workshop NLP-COVID

Reviews:

final_score: rating: 5: Marginally below acceptance threshold
review_content: This paper proposes a real-time Twitter visualization tool which consists of three components: Twitter classification, geolocation extraction and interactive visualization. 

I agree that the proposed system can be of great value in practical use. However, a lot of critical information is not included in this paper and without these information, it is hard to determine the contribution of this paper. 

First, there exist a lot of twitter mapping/visualization works, e.g., real-time crisis mapping of natural disasters using social media and this one: https://blog.tensorflow.org/2019/09/disaster-watch-crisis-mapping-platform.html. All these tools have similar structure as that of the proposed tool: extract location of the twitter, perform classification on the twitter content and visualize the classified twitters in a map. The only difference to me is that the target labels in the classification layer is different. The author should include a comprehensive review of these visualization tools and highlight the differences between this paper and previous tools. 

Second, a claim in this paper is that using negative L2 distances in the output classification layer outperforms using traditional inner product similarity. However, there is not enough experiments to support this claim. The marginal improvement in terms of validation accuracy (76.84 vs 76.35) is not convincing to me.

Finally, as the proposed system works in an interative and real-time manner, there should be an evaluation on the latency of the system.
review_title: Critical information is needed

final_score: rating: 4: Ok but not good enough - rejection
review_content: Even though the idea is interesting. But they are several issues that need to be addressed before considering it for publication.  for example  “training data” is not well described. The author mentioned that the annotated data is from Middle Eastern Respiratory Syndrome (MERS) outbreak but there is no clear connection with Covid-19 data, there are several Covid-19 datasets and the author did not mention why did not use them.  

Main point: author did not report any classifier’s performance or any kind of evaluation metrics.  even though it is short paper but at least a table explaining the performance metrics and how the classifier performs. The classification is heart of this visualization and the performance metric s are must for any NLP or applied NLP research paper.
review_title: Good idea but lack of analysis and there is no evaluation 

final_score: rating: 7: Good paper, accept
review_content: **Summary**
This work presented an interactive visualization system for COVID information including the number of affected people, prevention, treatment, death reports, etc. The system was based on Tweet streaming data with 4 COVID related filtering keywords. To classify tweets into different information categories (eg. number of affected people), the BERT-based pre-trained model was fined-tuned on the 2014 MERS dataset and the L2 layer was employed as an output layer. To further generate the information distributions on the global map, geolocations were extracted with name entity recognition from the text contents of tweets.

In this reviewer's view, this work is well written. This novel system provides a live information distribution that presents the changes in the pandemic situation. 

**Comments**
1. To extract text-based geolocation, are tweets with misinformation filtered out? People may discuss things in other places, which may not true. 

2. For the tweets that are not the first-person narrative, how do the maps of 'affected_people', 'deaths_reports' reflect the changes of the situation?
review_title: Real-time classification, geolocation and interactive visualization of COVID-19 information shared on social media to better understand global developments