Social media platforms, due to the prevalence, have become staging grounds for social warfare as antagonistic agents use social media to deceive large segments of population. Of specific concern are the coordinated campaigns orchestrated using groups of accounts to propagate false narratives. It has been observed that the coordinated campaigns by groups of social media users usually have common underlying emotions. As part of this research, we have used state-of-the-art deep learning Transformer models to extract fine grained emotions from users’ social media posts which we subsequently use to perform community detection to identify coordinated campaigns. We developed and tested our proposed methodology on a set of real-world Reddit posts. Our approach shows promise in using such a methodology to detect groups of similar users based on the emotion expressed in their social media posts. The details of our approach are described next. To start the analysis, we collected a set of Reddit public posts on a topic. Next, we used sophisticated emotion extraction to enrich the text of user social media posts. Our post emotion classification annotates social media posts and users with 27 fine-grained emotions such as optimistic, prideful, relieved, and gracious. This model can achieve state-of-the-art performance, because it uses the robust Transformer architecture that is initialized with a language learning task. Through successive layers of contextualization, the model can adapt to any natural language task. The model is first trained on a missing word task where a word in a sentence is masked and the model must predict the missing word. By doing this task on 11,000 books and the entirety of Wikipedia, the model has a strong, task-independent knowledge of the English language. Once the difficult part of teaching the model English is complete, we fine-tune the model to our task to produce a robust emotion tagging model. Once the posts are enriched and tagged with their emotion details, we use our community detection module to form communities around the extracted emotions based on social media behavior. To do this, we developed an embedding-based approach where users and emotions are translated into vector space embeddings and we use advanced mathematical operations to connect users to emotions. This allows us to identify group members in ways that go beyond plain keyword match. We extended out work to further identify influential users within a community. The process of detecting influential users is performed through a PageRank-like algorithm where connections between posts and users are leveraged to identify the key posts and users in a community. Initial experiments have shown promising results. Our emotion detector can accurately extract emotion information from posts and users. Our community detection algorithm can identify key subcommunities in a set of posts. Our influential users/post detection scores correlate strongly with Reddit social media indicators of influence.

Social Media Community Detection based on User Emotion

This is the 3rd of four poster sessions

**Date & Time:**  Thursday March 25th,  8-9:30am PDT

**Location:** Poster Session Room 2A

The live poster sessions will happen in our Gather.Town lounge at the designated time. Each session has their own room. Once you click the gathertown button below, you will need to navigate to the Poster Session Room for each session located on the right side of the lounge. Each speaker will be by their poster where you can communicate with them and view their poster by clicking X on your keyboard.

[![](https://assets.underline.io/uploads/markdown_image/1/image/737a34c989ecf3ae95182670d99c072c.png)](https://gather.town/app/5IAViMZXC9ZF4E4l/NAML)

Poster Session 2A

poster

Naval Information Warfare Center Pacific is partnering with AFCEA San Diego chapter to host the workshop.

The NAML workshop is an opportunity for researchers from Navy research and development centers and our partner organizations to exchange technical ideas and to come together as a team.

The goals of NAML are:

* To spread awareness of current machine learning research relevant to Naval applications

* To connect machine learning researchers with experts in military requirements

* To build and strengthen collaborations within the defense research community

**Watch this video to hear about the AFCEA purpose.**

<iframe width=1280 height=706 frameborder="0" scrolling="no" src="https://screencast-o-matic.com/player/creleVVcvRh?v=6&ff=1&title=0&controls=1" allowfullscreen="true"></iframe>

Alex Rosenfeld

1

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Social Media Community Detection based on User Emotion

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