| [ | |
| { | |
| "file": "paper_45.txt", | |
| "start": 533, | |
| "end": 969, | |
| "label": "Coherence", | |
| "user": "Ed", | |
| "text": "Mathew et al. (2019) collect and handcode 6,898 counter hate comments from YouTube videos targeting Jews, Blacks and LGBT communities. Ziems et al. (2020) use a collection of hate and counter hate keywords relevant to COVID-19 and create a dataset containing 359 counter hate tweets targeting Asians. Garland et al. (2020) work with German tweets and define hate and counter speech based on the communities to which the authors belong. " | |
| }, | |
| { | |
| "file": "paper_45.txt", | |
| "start": 1244, | |
| "end": 1339, | |
| "label": "Unsupported claim", | |
| "user": "Ed", | |
| "text": "Even if it were, conclusions and models from synthetic data may not transfer to the real world." | |
| }, | |
| { | |
| "file": "paper_45.txt", | |
| "start": 1834, | |
| "end": 2159, | |
| "label": "Coherence", | |
| "user": "Ed", | |
| "text": " Gao and Huang (2017) annotate hateful comments in the nested structures of 10 Fox News discussion threads. Vidgen et al. (2021) Utilizing conversational context has also been explored in text classification tasks such as sentiment analysis (Ren et al., 2016), stance (Zubiaga et al., 2018) and sarcasm (Ghosh et al., 2020)." | |
| } | |
| ] |