most citedData Sets: Word Embeddings Learned from Tweets and General Data

17 citations · 34 across the 5 of their papers we have counts for

collaborators

5 papers

cs.IR20195 cited

Rumor Detection on Social Media: Datasets, Methods and Opportunities

Quanzhi Li, Qiong Zhang, Luo Si +1

Social media platforms have been used for information and news gathering, and they are very valuable in many applications. However, they also lead to the spreading of rumors and fa…

cs.CL2019

Uncover Sexual Harassment Patterns from Personal Stories by Joint Key Element Extraction and Categorization

Yingchi Liu, Quanzhi Li, Marika Cifor +3

The number of personal stories about sexual harassment shared online has increased exponentially in recent years. This is in part inspired by the \#MeToo and \#TimesUp movements. S…

cs.SI20178 cited

Reuters Tracer: Toward Automated News Production Using Large Scale Social Media Data

Xiaomo Liu, Armineh Nourbakhsh, Quanzhi Li +3

To deal with the sheer volume of information and gain competitive advantage, the news industry has started to explore and invest in news automation. In this paper, we present Reute…

cs.SI20174 cited

"Breaking" Disasters: Predicting and Characterizing the Global News Value of Natural and Man-made Disasters

Armineh Nourbakhsh, Quanzhi Li, Xiaomo Liu +1

Due to their often unexpected nature, natural and man-made disasters are difficult to monitor and detect for journalists and disaster management response teams. Journalists are inc…

cs.CL201717 cited

Data Sets: Word Embeddings Learned from Tweets and General Data

Quanzhi Li, Sameena Shah, Xiaomo Liu +1

A word embedding is a low-dimensional, dense and real- valued vector representation of a word. Word embeddings have been used in many NLP tasks. They are usually gener- ated from a…