# Detection of Dangerous Events on Social Media: A Perspective Review

arXiv: https://arxiv.org/abs/2204.01351 · HTML: https://mlsanity.com/paper/2204.01351/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 66 references that are in the map.
- First posted on arXiv 4 Apr 2022.

## Builds on (most cited first, 19 of 19)

- [MAVEN: A Massive General Domain Event Detection Dataset](https://mlsanity.com/paper/2004.13590/) (2020)
- [Latent Dirichlet Allocation (LDA) and Topic modeling: models, applications, a survey](https://mlsanity.com/paper/1711.04305/) (2017)
- [Analyzing Polarization in Social Media: Method and Application to Tweets on 21 Mass Shootings](https://mlsanity.com/paper/1904.01596/) (2019)
- [Uncovering Coordinated Networks on Social Media: Methods and Case Studies](https://mlsanity.com/paper/2001.05658/) (2020)
- [CrisisBERT: a Robust Transformer for Crisis Classification and Contextual Crisis Embedding](https://mlsanity.com/paper/2005.06627/) (2020)
- [Euphemistic Phrase Detection by Masked Language Model](https://mlsanity.com/paper/2109.04666/) (2021)
- [Multi-Task Learning with Sentiment, Emotion, and Target Detection to Recognize Hate Speech and Offensive Language](https://mlsanity.com/paper/2109.10255/) (2021)
- [Unsupervised Detection of Sub-events in Large Scale Disasters](https://mlsanity.com/paper/1912.13332/) (2019)
- [Slipping to the Extreme: A Mixed Method to Explain How Extreme Opinions Infiltrate Online Discussions](https://mlsanity.com/paper/2109.00302/) (2021)
- [Crowdsourcing Cybersecurity: Cyber Attack Detection using Social Media](https://mlsanity.com/paper/1702.07745/) (2017)
- [EventKG+Click: A Dataset of Language-specific Event-centric User Interaction Traces](https://mlsanity.com/paper/2010.12370/) (2020)
- [Efficacy of BERT embeddings on predicting disaster from Twitter data](https://mlsanity.com/paper/2108.10698/) (2021)
- ["Breaking" Disasters: Predicting and Characterizing the Global News Value of Natural and Man-made Disasters](https://mlsanity.com/paper/1709.02510/) (2017)
- [#StayHome or #Marathon? Social Media Enhanced Pandemic Surveillance on Spatial-temporal Dynamic Graphs](https://mlsanity.com/paper/2108.03670/) (2021)
- [Mining Social Media to Inform Peatland Fire and Haze Disaster Management](https://mlsanity.com/paper/1706.05406/) (2017)
- [Understanding and Monitoring Human Trafficking via Social Sensors: A Sociological Approach](https://mlsanity.com/paper/1805.10617/) (2018)
- [Event Detection and Retrieval on Social Media](https://mlsanity.com/paper/1807.03675/) (2018)
- [Measuring the Impact of Influence on Individuals: Roadmap to Quantifying Attitude](https://mlsanity.com/paper/2010.13304/) (2020)
- [An Exploration of Geo-temporal Characteristics of Users' Reactions on Social Media During the Pandemic](https://mlsanity.com/paper/2103.13032/) (2021)

## Abstract (arXiv)

Social media is an essential gateway of information and communication for people worldwide. The amount of time spent and reliance of people on social media makes it a vital resource for detecting events happening in real life. Thousands of significant events are posted by users every hour in the form of multimedia. Some individuals and groups target the audience to promote their agenda among these users. Their cause can threaten other groups and individuals who do not share the same views or have specific differences. Any group with a definitive cause cannot survive without the support which acts as a catalyst for their agenda. A phenomenon occurs where people are fed information that motivates them to act on their behalf and carry out their agenda. One is benefit results in the loss of the others by putting their lives, assets, physical and emotional health in danger. This paper introduces a concept of dangerous events to approach this problem and their three main types based on their characteristics: action, scenarios, and sentiment-based dangerous events.