---
title: "GCAN — paper by Lu and Li (2020)"
description: "The authors propose GCAN (Graph-aware Co-Attention Networks), which uses three inputs: the tweet… 16% accuracy. 33 references, cited by 63 papers here."
authors: ["Yi-Ju Lu", "Cheng-Te Li"]
year: 2020
venue: "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics"
source: "https://aclanthology.org/2020.acl-main.48/"
doi: "10.18653/v1/2020.acl-main.48"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/lu-2020-gcan-graph-aware-co-attention/"
---
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# GCAN: Graph-aware Co-Attention Networks for Explainable Fake News Detection on Social Media

**Authors:** Yi-Ju Lu and [Cheng-Te Li](https://md.research.elamur.ai/free/en/authors/cheng-te-li/) · **Year:** 2020 · **Venue:** [Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/acl/) ([ACL 2020](https://md.research.elamur.ai/free/en/venues/acl/2020/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2020.acl-main.48/) · [PDF](https://aclanthology.org/2020.acl-main.48.pdf) · [DOI 10.18653/v1/2020.acl-main.48](https://doi.org/10.18653/v1/2020.acl-main.48) · [OpenAlex](https://openalex.org/W3017402509)

**Citations:** 405 citations (OpenAlex, 2026-10-14) · [63](https://md.research.elamur.ai/free/en/papers/lu-2020-gcan-graph-aware-co-attention/cited-by/) in our library, of the 141,840 papers whose bibliographies we read

## Summary
The paper tackles fake news detection on Twitter in a realistic but difficult setting. The source post is a short tweet, retweeters usually add no comments, and the social graph and diffusion tree are often hidden for privacy reasons. The authors propose GCAN (Graph-aware Co-Attention Networks), which uses three inputs: the tweet text, the ordered sequence of users who retweeted it, and features from those users' profiles.

The model has several parts:
- A GRU encodes the source words.
- CNN and GRU branches model how the retweet chain unfolds.
- A GCN runs over a constructed user-interaction graph.
- A dual co-attention mechanism links the source text with both the propagation and the interaction representations.

On real Twitter datasets, GCAN beats state-of-the-art baselines by about 16% accuracy on average. Its attention weights also point to suspicious retweeters and to the words they focus on. This matters because platforms or agencies can see why a story was flagged without needing long articles or user comments.

## Key points
- Detection runs under three constraints: short-text source tweets, no comment text, and no social or diffusion network structure. Only the retweet order and user profiles are used.
- Experiments use the Twitter15 and Twitter16 datasets. The authors report an average accuracy gain of roughly 16% over strong prior models, and say GCAN also does well at early detection.
- Ablation tests show that every component helps. The largest drops come from removing the dual co-attention, the propagation/interaction representations, or the source tweet itself.
- In the case studies, suspicious users tend to be unverified, to have newer accounts and shorter profile descriptions, and to sit close to the original poster in the graph. Fake tweets draw attention to words like "breaking" and "strict", while real ones draw it to "confirmed" and "irrelevant".
- The code is released at github.com/l852888/GCAN. The authors suggest GCAN could carry over to other short-text tasks such as sentiment analysis, hate speech detection, and popularity prediction.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [National Cheng Kung University](https://md.research.elamur.ai/free/en/universities/national-cheng-kung-university/) — University, Tainan, Taiwan · 3 works · 613 citations

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## Cite

Cite as: Yi-Ju Lu and Cheng-Te Li. 2020. GCAN: Graph-aware Co-Attention Networks for Explainable Fake News Detection on Social Media. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{lu2020gcan,
  title = {{GCAN: Graph-aware Co-Attention Networks for Explainable Fake News Detection on Social Media}},
  author = {Yi-Ju Lu and Cheng-Te Li},
  booktitle = {Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics},
  year = {2020},
  doi = {10.18653/v1/2020.acl-main.48},
  url = {https://doi.org/10.18653/v1/2020.acl-main.48}
}
```

- [← previous Data Augmentation using Pre-trained Transformer Models](https://md.research.elamur.ai/free/en/papers/kumar-2020-data-augmentation-using-pre-trained/)
- [next → HERO: Hierarchical Encoder for Video+Language Omni-representation Pre-training](https://md.research.elamur.ai/free/en/papers/li-2020-hero-hierarchical-encoder-video-language/)
- [↑ back to papers of 2020](https://md.research.elamur.ai/free/en/papers/2020/page/2/)

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33 references, 5 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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*Updated 24 October 2026*