├── README.md
├── TUMCC-clean.txt
└── TUMCC-raw.7z


/README.md:
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 1 | # TUMCC (Telegram Underground Market Chinese Corpus)
 2 | 
 3 | TUMCC is the first Chinese corpus in the jargon identification field. 
 4 | 
 5 | **28,749** sentences, including **804,971** characters, from **19,821** Telegram users of **12** Telegram groups were collected when we built TUMCC.
 6 | 
 7 | We had finished data screening and word segmentation before we released this corpus. So it might be easier for you to use.
 8 | 
 9 | After cleaning, TUMCC contains 3,863 sentences (100,000 characters) from 3,139 Telegram users.
10 | 
11 | ## Files
12 | 
13 | ``TUMCC-clean.txt`` contains the corpus after our cleaning. You can use it directly in your research.
14 | 
15 | ``TUMCC-raw.7z`` contains raw information we collected from Telegram. You can do text cleaning to get more valid data and valuable information.
16 | 
17 | For more details about the target Telegram group sources for data extraction, please refer to the paper [`Identification of Chinese Dark Jargons in Telegram Underground Markets Using Context-Oriented and Linguistic Features`](https://doi.org/10.1016/j.ipm.2022.103033) ([Information Processing and Management](https://www.sciencedirect.com/journal/information-processing-and-management), 2022).
18 | 
19 | ## Citation
20 | Thanks for your interest in our dataset, please feel free to leave a ⭐️ or cite us through:
21 | 
22 | ```
23 | @article{hou2022identification,
24 |   title={Identification of Chinese dark jargons in Telegram underground markets using context-oriented and linguistic features},
25 |   author={Hou, Yiwei and Wang, Hailin and Wang, Haizhou},
26 |   journal={Information Processing \& Management},
27 |   volume={59},
28 |   number={5},
29 |   pages={103033,1--20},
30 |   year={2022},
31 |   publisher={Elsevier}
32 | }
33 | ```
34 | 


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/TUMCC-raw.7z:
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https://raw.githubusercontent.com/m1-llie/TUMCC/637d7deaa308426061d0220efd0d638cc411f8e3/TUMCC-raw.7z


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