dEFEND: Explainbale Fake News Detection

In recent years, to mitigate the problem of fake news, computational detection of fake news has been studied, producing some promising early results. While important, however, we argue that a critical missing piece of the study be the explainability of such detection, i.e., why a particular piece of news is detected as fake. In this paper, therefore, we study the explainable detection of fake news. We develop a sentence-comment co-attention sub-network to exploit both news contents and user comments to jointly capture explainable top-k check-worthy sentences and user comments for fake news detection. We conduct extensive experiments on real-world datasets and demonstrate that the proposed method not only significantly outperforms several state-of-the-art fake news detection methods. Code and Results.
References
@inproceedings{shu2019defend,
  title={dEFEND: Explainable Fake News Detection},
  author={Shu, Kai and Cui, Limeng and Wang, Suhang and Lee, Dongwon and Liu, Huan},
  booktitle={Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery \& Data Mining},
  year={2019},
  organization={ACM} }

Fake News Detection

We released a tool FakeNewsTracker, for collecting, analyzing, and visualizing of fake news and the related dissemination on social media!
The latest dataset paper with detailed analysis on the dataset can be found at FakeNewsNet.
FakeNewsNet is a benchmark data repository fake news detection, which contains information of news content, social context, and spatialtemporal information for studying fake news on social media. Data and APIs are available at Github.
References
If you use this dataset, please consider cite the following papers:
@article{shu2018fakenewsnet,
  title={FakeNewsNet: A Data Repository with News Content, Social Context and Dynamic Information for Studying Fake News on Social Media},
  author={Shu, Kai and Mahudeswaran, Deepak and Wang, Suhang and Lee, Dongwon and Liu, Huan},
  journal={arXiv preprint arXiv:1809.01286},
  year={2018} }
@article{shu2017fake,
title={Fake News Detection on Social Media: A Data Mining Perspective},
  author={Shu, Kai and Sliva, Amy and Wang, Suhang and Tang, Jiliang and Liu, Huan},
  journal={ACM SIGKDD Explorations Newsletter},
  volume={19},
  number={1},
  pages={22--36},
  year={2017},
  publisher={ACM} }

Sacarsm Detection with Emojis

Sarcasm detection on social media is important for users to understand the underlying messages. The majority of existing sarcasm detection algorithms focus on text information; while emotion information expressed such as emojis are ignored. We release a new dataset for our SBP paper on sarcasm detection on social media with emoji information. Data and code are available at Github.
References
If you use this dataset, please cite the following paper:
@inproceedings{jay2019sarsam,
  title={Exploiting Emojis for Sarcasm Detection},
  author={Subramanian, Jayashree and Sridharan, Varun and Shu, Kai and Liu, Huan},
  booktitle={International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation}
  year={2019},
  organization={Springer} }

Cross Media Friend & Item Recommendations

Friend and item recommendation on a social media site is an important task, which not only brings conveniences to users but also benefits platform providers. However, recommendation for newly launched social media sites is challenging because they often lack user historical data and encounter data sparsity and cold-start problem. Thus, it is important to exploit auxiliary information to help improve recommendation performances on these sites. We construct a new dataset that ensure that both source and target sites have the following information: user-item interactions, user-user relations, and item features. Raw Data are available at Book  Movie.
References
If you use this dataset, please cite the following paper:
@inproceedings{shu2018crossfire,   title={Crossfire: Cross media joint friend and item recommendations},
  author={Shu, Kai and Wang, Suhang and Tang, Jiliang and Wang, Yilin and Liu, Huan},
  booktitle={Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining},
  pages={522--530},
  year={2018},
  organization={ACM} }