AggregHate: An Efficient Aggregative Approach for the Detection of Hatemongers on Social Platforms
Authors: Tom Marzea, Abraham Israeli, Eyal Arviv, Oren Tsur


Reviewer paper summaries: The paper gives a multimodal approach for detecting hate-mongers on social platforms, focusing on user-level analysis rather than individual post  analysis. The methodology uses user texts, activity, and network information to improve the detection of hate speech. The method is evaluated on three datasets: Twitter, Gab, and Parler, and shows improved detection of hate-mongers compared to traditional text and graph-based methods.
Summary of strengths: The shift from post-level to user-level analysis is  novel. This perspective can use the broader context of user behavior, making the detection of subtle hate speech more effective.

The combination of textual, activity, and network data provides a overall view of user behavior. 

The use of three distinct social media sources (Twitter, Gab, Parler) increases the generalizability of the findings. It shows the method's applicability across different social platforms.
Summary of weaknesses: I am concerned about a few things.

1. Baseline coverage : Authors mention works like SHEILD (2024) which are more recent, but do not show comparision with it. The latest baselines used are from 2021 (or so it seems like). I just want authors to ensure they have compared against the most latest methods. 

2. A in-depth error analysis with qualitative analysis would be appreciated. I'd like to see the false positives and false negatives -- where did the model make mistakes. 

3. Reproducibility : I have not seen the dataset / reproducibility package in the code. Please provide the anonymous link to the code.
Comments, suggestions and typos: Minor : The font seems off. I am not sure if there is some issue with the latex template, but please fix it.
Overall assessment scores: 2.5