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Civil Unrest Case Study

Code from "Study of Manifestation of Civil Unrest on Twitter" W-NUT @ EMNLP 2021

Data

Global Civil Unrest on Twitter

The case studies and civil unrest models were trained on a new dataset introduced in the paper, Global Civil Unrest on Twitter (G-CUT). The dataset is available for download here.

As per Twitter TOS, we do not supply the full tweet. We include:

  • Tweet ID
  • Creation date
  • Country of origin (code in ISO Alpha 2, as specified in the tweet geotag)
  • Country of origin (code in ISO Alpha 3 format)
  • Civil unrest filtration model score as provided by the BERTweet model from Sech et al 2020
  • Civil unrest related: a convenience boolean whether the civil unrest filtration model score is >= 0.5

We suggest using a tool like Hydrator to re-collect the tweets.

Countries included in the dataset:

Algeria, Angola, Bangladesh, Benin, Burkina Faso, Burundi, Cambodia, Cameroon, Central African Republic, Chad, Democratic Republic of Congo, Egypt, Ethiopia, Ghana, Guinea, Ivory Coast, Kenya, Liberia, Libya, Madagascar, Malawi, Mali, Morocco, Mozambique, Myanmar, Namibia, Nepal, Niger, Nigeria, Pakistan, Senegal, Sierra Leone, Somalia, South Africa, Sri Lanka, Sudan, Tanzania, Thailand, Togo, Tunisia, Uganda, Zambia

Country All / Filtered Country All / Filtered Country All / Filtered
AGO 289,373 / 16,639 BDI 25,041 / 6,097 BEN 150,348 / 16,489
BFA 50,197 / 4,327 BGD 2,318,969 / 330,947 CAF 28,723 / 2,860
CIV 564,197 / 34,286 CMR 833,795 / 71,420 COD 251,430 / 27,920
DZA 1,561,362 / 150,084 EGY 8,845,187 / 608,977 ETH 261,202 / 59,111
GHA 11,772,277 / 1,711,557 GIN 68,168 / 4,652 KEN 11,837,021 / 2,451,866
KHM 658,744 / 86,021 LBR 114,329 / 20,267 LBY 674,195 / 55,177
LKA 2,312,676 / 320,593 MAR 2,155,938 / 243,772 MDG 118,673 / 10,622
MLI 77,523 / 6,782 MMR 552,406 / 81,130 MOZ 349,321 / 31,634
MWI 780,767 / 101,891 NAM 1,881,238 / 238,720 NER 58,960 / 5,220
NGA 48,954,857 / 9,660,532 NPL 1,789,592 / 239,200 PAK 15,927,538 / 2,966,772
SDN 743,925 / 55,151 SEN 684,381 / 40,840 SLE 131,235 / 19,460
SOM 215,610 / 60,061 TCD 26,951 / 2,577 TGO 58,253 / 8,254
THA 14,661,060 / 980,846 TUN 944,833 / 87,903 TZA 2,216,871 / 248,856
UGA 3,274,687 / 638,372 ZAF 72,155,722 / 9,323,649 ZMB 1,706,438 / 246,815

ACLED

The ground-truth ACLED dataset can be gathered from https://acleddata.com/data-export-tool/ with the following parameters:

  • Event type: Riots and Protests
  • Years: 2014 - 2019
  • Countries: codes listed in the table above

We also include the data /data/2014-01-01-2020-01-01_acled_reduced_all.csv.

Code

We have included the code and notebooks to reproduce our analyses and models in /code and /notebooks. The location debiasing features are in /data/location_stopwords.txt (combined with standard English stopwords).

If you have any questions, please contact Alexandra DeLucia via email aadelucia@jhu.edu