Name
Abstract | ||
|---|---|---|
The fast expanding of social media fuels the spreading of misinformation which disrupts peopleu0027s normal lives. It is urgent to achieve goals of misinformation identification and early detection in social media. In dynamic and complicated social media scenarios, some conventional methods mainly concentrate on feature engineering which fail to cover potential features in new scenarios and have difficulty in shaping elaborate high-level interactions among significant features. Moreover, a recent Recurrent Neural Network (RNN) based method suffers from deficiencies that it is not qualified for practical early detection of misinformation and poses a bias to the latest input. In this paper, we propose a novel method, Convolutional Approach for Misinformation Identification (CAMI) based on Convolutional Neural Network (CNN). CAMI can flexibly extract key features scattered among an input sequence and shape high-level interactions among significant features, which help effectively identify misinformation and achieve practical early detection. Experiment results on two large-scale datasets validate the effectiveness of CAMI model on both misinformation identification and early detection tasks. |
Year | Venue | Field |
|---|---|---|
2017 | IJCAI | Early detection,Social media,Convolutional neural network,Computer science,Recurrent neural network,Misinformation,Feature engineering,Artificial intelligence,Machine learning |
DocType | Citations | PageRank |
Conference | 6 | 0.42 |
References | Authors | |
12 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Feng Yu | 1 | 36 | 10.95 |
| Qiang Liu | 2 | 157 | 11.03 |
| Shu Wu | 3 | 469 | 34.74 |
| Liang Wang | 4 | 4317 | 243.28 |
| Tieniu Tan | 5 | 11681 | 744.35 |