China University Online Public Opinion Risk Dataset

With the widespread popularity of social software and self-media, online public opinion incidents in colleges and universities occur frequently and present a complicated situation. In the big data era, university students have gained a more relaxed environment in which to receive and disseminate public opinion information, enabling them to spread their opinions and insights to the Internet more rapidly, thus exacerbating the riskiness of public opinion information dissemination. We constructed CUOPO, the first risk classification dataset of China university online public opinion, and screened out 10,255 representative public opinion texts from a large number of university online public opinion information, including 3,641 risk-free and 6,614 risky texts. These risky texts cover many fields, including 1,755 college livelihood risk texts, 767 campus safety risk texts, 1,395 school order risk texts, 906 university reputation risk texts, and 1,793 advertisement risk texts. The dataset contains various information about each network opinion, including authentic labels, text information, time information, and network information. Through an in-depth study of CUOPO, we found that universities have significant risk issues in the areas of livelihood, safety, teaching order, reputation, and advertisement diversion, which require great attention from university administrators. To validate the effectiveness of the CUOPO, we conduct extensive experiments on the dataset using a series of neural network methods to provide benchmark results for predicting online public opinion risk texts. We expect that CUOPO can provide strong data support for the study of the types of online public opinion risks in colleges and universities and thus play a positive role in promoting the progress of college and university public opinion research. The dataset is available at https://github.com/TianShengLee98/CUOPO-Dataset.