# FakeFlow: Fake News Detection by Modeling the Flow of Affective Information

arXiv: https://arxiv.org/abs/2101.09810 · HTML: https://mlsanity.com/paper/2101.09810/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 28 references that are in the map.
- First posted on arXiv 24 Jan 2021.

## Builds on (most cited first, 18 of 18)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Longformer: The Long-Document Transformer](https://mlsanity.com/paper/2004.05150/) (2020)
- [Automatic Detection of Fake News](https://mlsanity.com/paper/1708.07104/) (2017)
- [The emotional arcs of stories are dominated by six basic shapes](https://mlsanity.com/paper/1606.07772/) (2016)
- [DocBERT: BERT for Document Classification](https://mlsanity.com/paper/1904.08398/) (2019)
- [OpenTag: Open Attribute Value Extraction from Product Profiles [Deep Learning, Active Learning, Named Entity Recognition]](https://mlsanity.com/paper/1806.01264/) (2018)
- [This Just In: Fake News Packs a Lot in Title, Uses Simpler, Repetitive Content in Text Body, More Similar to Satire than Real News](https://mlsanity.com/paper/1703.09398/) (2017)
- [Fact-Checking Meets Fauxtography: Verifying Claims About Images](https://mlsanity.com/paper/1908.11722/) (2019)
- [Stop Clickbait: Detecting and Preventing Clickbaits in Online News Media](https://mlsanity.com/paper/1610.09786/) (2016)
- [A Survey of Fake News: Fundamental Theories, Detection Methods, and Opportunities](https://mlsanity.com/paper/1812.00315/) (2018)
- [Fully Automated Fact Checking Using External Sources](https://mlsanity.com/paper/1710.00341/) (2017)
- [A Topic-Agnostic Approach for Identifying Fake News Pages](https://mlsanity.com/paper/1905.00957/) (2019)
- [Proppy: A System to Unmask Propaganda in Online News](https://mlsanity.com/paper/1912.06810/) (2019)
- [An Emotional Analysis of False Information in Social Media and News Articles](https://mlsanity.com/paper/1908.09951/) (2019)
- [Simple Open Stance Classification for Rumour Analysis](https://mlsanity.com/paper/1708.05286/) (2017)
- [Towards Detecting Rumours in Social Media](https://mlsanity.com/paper/1504.04712/) (2015)
- [Letting Emotions Flow: Success Prediction by Modeling the Flow of Emotions in Books](https://mlsanity.com/paper/1805.09746/) (2018)
- [Folksonomication: Predicting Tags for Movies from Plot Synopses Using Emotion Flow Encoded Neural Network](https://mlsanity.com/paper/1808.04943/) (2018)

## Built on by (most cited first, 20 of 20)

- [MMFakeBench: A Mixed-Source Multimodal Misinformation Detection Benchmark for LVLMs](https://mlsanity.com/paper/2406.08772/) (2024)
- [FKA-Owl: Advancing Multimodal Fake News Detection through Knowledge-Augmented LVLMs](https://mlsanity.com/paper/2403.01988/) (2024)
- [Integrating Pattern- and Fact-based Fake News Detection via Model Preference Learning](https://mlsanity.com/paper/2109.11333/) (2021)
- [Emotion Detection for Misinformation: A Review](https://mlsanity.com/paper/2311.00671/) (2023)
- [Learn over Past, Evolve for Future: Forecasting Temporal Trends for Fake News Detection](https://mlsanity.com/paper/2306.14728/) (2023)
- [How Vulnerable Are Automatic Fake News Detection Methods to Adversarial Attacks?](https://mlsanity.com/paper/2107.07970/) (2021)
- [Studying Fake News Spreading, Polarisation Dynamics, and Manipulation by Bots: a Tale of Networks and Language](https://mlsanity.com/paper/2109.07909/) (2021)
- [Using Persuasive Writing Strategies to Explain and Detect Health Misinformation](https://mlsanity.com/paper/2211.05985/) (2022)
- [Detect, Investigate, Judge and Determine: A Knowledge-guided Framework for Few-shot Fake News Detection](https://mlsanity.com/paper/2407.08952/) (2024)
- [T^2Agent A Tool-augmented Multimodal Misinformation Detection Agent with Monte Carlo Tree Search](https://mlsanity.com/paper/2505.19768/) (2025)
- [KEN: Knowledge Augmentation and Emotion Guidance Network for Multimodal Fake News Detection](https://mlsanity.com/paper/2507.09647/) (2025)
- [Multimodal Dual Emotion with Fusion of Visual Sentiment for Rumor Detection](https://mlsanity.com/paper/2204.11515/) (2022)
- [Experiments in News Bias Detection with Pre-Trained Neural Transformers](https://mlsanity.com/paper/2406.09938/) (2024)
- [Rumor Detection by Multi-task Suffix Learning based on Time-series Dual Sentiments](https://mlsanity.com/paper/2502.14383/) (2025)
- [Towards Robust and Realible Multimodal Misinformation Recognition with Incomplete Modality](https://mlsanity.com/paper/2510.05839/) (2025)
- [Expanding the Role of Affective Phenomena in Multimodal Interaction Research](https://mlsanity.com/paper/2305.10827/) (2023)
- [BiasScanner: Automatic Detection and Classification of News Bias to Strengthen Democracy](https://mlsanity.com/paper/2407.10829/) (2024)
- [The Veracity Problem: Detecting False Information and its Propagation on Online Social Media Networks](https://mlsanity.com/paper/2409.03948/) (2024)
- [MAPX: An explainable model-agnostic framework for the detection of false information on social media networks](https://mlsanity.com/paper/2409.08522/) (2024)
- [LLM-based Contrastive Self-Supervised AMR Learning with Masked Graph Autoencoders for Fake News Detection](https://mlsanity.com/paper/2508.18819/) (2025)

## Abstract (arXiv)

Fake news articles often stir the readers' attention by means of emotional appeals that arouse their feelings. Unlike in short news texts, authors of longer articles can exploit such affective factors to manipulate readers by adding exaggerations or fabricating events, in order to affect the readers' emotions. To capture this, we propose in this paper to model the flow of affective information in fake news articles using a neural architecture. The proposed model, FakeFlow, learns this flow by combining topic and affective information extracted from text. We evaluate the model's performance with several experiments on four real-world datasets. The results show that FakeFlow achieves superior results when compared against state-of-the-art methods, thus confirming the importance of capturing the flow of the affective information in news articles.