Datasets:

m2im
/

multilingual-twitter-collective-violence-dataset

tweetid int64	lang string	post1geo10 int64	post1geo20 int64	post1geo30 int64	post1geo50 int64	post1geo70 int64	post2geo10 int64	post2geo20 int64	post2geo30 int64	post2geo50 int64	post2geo70 int64	post3geo10 int64	post3geo20 int64	post3geo30 int64	post3geo50 int64	post3geo70 int64	post7geo10 int64	post7geo20 int64	post7geo30 int64	post7geo50 int64	post7geo70 int64	pre1geo10 int64	pre1geo20 int64	pre1geo30 int64	pre1geo50 int64	pre1geo70 int64	pre2geo10 int64	pre2geo20 int64	pre2geo30 int64	pre2geo50 int64	pre2geo70 int64	pre3geo10 int64	pre3geo20 int64	pre3geo30 int64	pre3geo50 int64	pre3geo70 int64	pre7geo10 int64	pre7geo20 int64	pre7geo30 int64	pre7geo50 int64	pre7geo70 int64
445,511,997,619,462,140	es	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	1
467,814,736,869,195,800	es	0	0	0	0	0	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
426,159,170,698,887,200	es	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1
414,488,555,461,681,150	ar	0	0	0	0	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	0	0	0	0	1	0	0	0	0	1	0	0	0	0	1	1	1	1	1	1
443,015,055,479,963,650	es	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1
446,010,348,907,556,860	es	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	1	1	0	0	0	0	0	0	0	0	0	1	0	0	0	0	1	0	0	0	0	1
421,442,106,239,250,400	en	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	1	0	0	0	0	1	0	0	0	0	1	0	0	0	0	1
452,108,061,621,362,700	es	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
450,961,216,824,156,160	es	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
442,308,093,381,447,700	es	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	1	1
385,079,291,181,924,350	es	0	0	0	0	0	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
400,393,079,729,164,300	ar	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
472,532,023,194,951,700	es	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1
450,762,835,044,618,240	es	0	0	0	0	0	0	0	0	0	0	1	1	1	1	1	1	1	1	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
377,870,432,256,262,140	en	0	0	0	1	1	0	0	0	1	1	0	0	0	1	1	0	0	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
447,241,661,488,566,300	es	0	0	0	0	0	0	0	0	0	1	0	0	0	0	1	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
451,054,653,338,554,400	pt	0	0	0	0	0	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
475,996,592,927,801,340	en	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	1	1
372,199,885,950,316,540	es	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1
382,025,496,449,388,540	en	0	0	1	1	1	0	0	1	1	1	0	0	1	1	1	0	0	1	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
419,848,106,323,484,700	es	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1
422,582,050,554,531,840	es	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	1	1	0	0	0	0	0	0	0	0	1	1	0	0	0	1	1	0	0	0	1	1
490,286,790,939,803,650	ru	0	0	0	1	1	0	0	0	1	1	0	0	0	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1
372,087,952,559,456,260	es	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	1
428,127,829,780,230,140	es	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	1	1	0	0	0	1	1	0	0	0	1	1
437,296,620,259,246,100	ru	0	0	0	0	0	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
458,692,245,202,673,660	ar	0	0	0	0	0	0	0	0	0	0	0	1	1	1	1	0	1	1	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
425,666,107,622,850,560	ar	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	0	0	1	1	1	0	0	1	1	1	0	0	1	1	1
451,518,983,788,036,100	pt	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
456,544,627,747,610,600	es	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
449,676,836,641,861,600	es	0	0	0	1	1	0	0	0	1	1	0	0	0	1	1	0	0	0	1	1	0	0	0	1	1	0	0	0	1	1	0	0	0	1	1	0	0	0	1	1
488,206,951,344,386,050	ar	0	0	0	0	1	0	0	0	0	1	0	0	0	0	1	1	1	1	1	1	0	0	0	0	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1
460,067,464,391,716,860	en	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	1	1
449,661,053,073,621,000	und	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1
420,318,896,827,015,200	es	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1
437,213,459,378,606,100	uk	0	0	0	0	0	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
493,136,273,973,215,200	ar	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	1	1	1	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
442,843,540,318,851,100	es	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1
479,813,127,723,565,060	es	0	0	0	0	0	0	0	0	0	0	1	1	1	1	1	1	1	1	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
414,684,974,718,386,200	en	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
424,981,341,776,908,300	es	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
492,018,853,925,232,640	en	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
394,629,149,702,488,060	ru	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
492,056,568,272,146,400	in	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
489,692,558,323,245,060	ar	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	1	1
445,354,195,336,847,360	es	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	1
390,248,177,795,743,740	es	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	1	1
380,118,685,764,968,450	ar	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
378,269,824,398,286,850	en	0	0	0	0	0	0	0	0	1	1	0	0	0	1	1	0	0	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
489,032,044,761,325,600	en	0	0	0	0	1	0	0	0	0	1	0	0	0	0	1	0	0	0	0	1	0	0	0	0	1	0	0	0	0	1	0	0	0	0	1	0	0	0	1	1
467,300,241,725,616,100	en	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1
392,465,874,113,667,100	es	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1
447,448,212,933,451,800	in	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1
480,657,176,445,599,740	en	0	0	0	1	1	0	0	0	1	1	0	0	0	1	1	0	0	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	1	1
492,464,508,808,335,360	ar	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
451,914,020,086,612,000	pt	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
407,717,760,978,804,740	es	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	1	1
363,254,146,004,025,340	tl	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1
486,585,659,058,962,400	en	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	1	1
445,682,756,954,357,760	ar	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	1	1	0	0	0	0	0	0	1	1	1	1	0	1	1	1	1	0	1	1	1	1
483,619,381,835,218,940	en	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
368,223,819,124,572,160	tl	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
412,715,933,963,411,460	es	0	0	1	1	1	0	0	1	1	1	0	0	1	1	1	1	1	1	1	1	0	0	1	1	1	0	0	1	1	1	0	0	1	1	1	0	0	1	1	1
427,840,274,979,885,060	es	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	1	1	0	0	0	1	1
422,662,649,961,533,440	ar	0	0	0	0	0	0	0	0	0	0	1	1	1	1	1	1	1	1	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
441,370,741,502,533,600	es	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	1	1
413,976,986,693,165,060	ar	0	0	0	0	0	0	0	0	0	1	0	0	0	0	1	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1
408,607,370,357,788,700	es	0	0	0	0	0	0	0	0	0	0	1	1	1	1	1	1	1	1	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	1	1
415,610,330,887,512,060	und	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
375,066,253,993,717,760	ar	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	1	0	1	1	1	1	0	1	1	1	1	0	1	1	1	1
429,443,520,429,318,140	es	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
456,403,147,179,565,060	ar	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	1	0	1	1	1	1
373,794,130,805,026,800	en	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1
446,329,816,850,382,850	ar	0	0	0	0	1	0	0	0	0	1	0	0	0	0	1	0	0	0	0	1	0	0	0	1	1	0	0	0	1	1	0	0	0	1	1	0	0	0	1	1
475,682,006,718,611,460	in	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1
370,398,514,146,447,360	en	0	0	0	0	1	0	0	0	0	1	0	0	0	0	1	1	1	1	1	1	0	0	0	0	1	0	0	0	0	1	0	0	0	0	1	0	0	0	0	1
479,714,517,560,332,300	es	0	0	0	0	0	0	0	0	1	1	0	0	0	1	1	0	0	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
418,992,112,219,086,850	es	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
429,615,393,788,137,500	es	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
447,834,219,751,157,760	es	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	0	0	1	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
483,617,111,886,295,040	es	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	1
446,092,894,068,564,000	es	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	1	1	0	0	0	0	1	0	0	0	0	1	0	0	0	0	1	0	0	0	0	1
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421,914,829,482,655,740	es	0	0	0	1	1	0	0	0	1	1	0	0	0	1	1	0	0	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1
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Dataset Card for Multilingual Twitter Dataset for Collective Violence Detection

This dataset links over 23 million multilingual tweets to a georeferenced database of collective violence events derived from the Uppsala Conflict Data Program (UCDP). It is designed for training and evaluating models that detect early indicators of organized violence in social media. Each tweet is labeled using a spatio-temporal heuristic across 40 multilabel indicators, specifying whether the tweet occurred near a violent event in space and time.

Dataset Details

Dataset Description

Curated by: Dr. Milton Mendieta and Dr. Timothy Warren
Funded by: Coalition for Open-Source Defense Analysis (CODA) Lab, Department of Defense Analysis, NPS
Shared by: Dr. Milton Mendieta and Dr. Timothy Warren
License: Other---Tweet text is excluded in compliance with the license agreement governing the ethical use of the Twitter archive for academic research
Language(s) (NLP): This multilingual dataset spans 68 languages, identified using ISO 639-1 codes. It also includes the und (undefined) tag, which is commonly used in X (formerly Twitter) metadata to denote unclassified language content, as shown in the table below. The language distribution closely mirrors the global linguistic composition of X during the study period.

Code	Language (ISO-639)
am	Amharic
ar	Arabic
bg	Bulgarian
bn	Bengali
bo	Tibetan
bs	Bosnian
chr	Cherokee
ckb	Central Kurdish (Sorani)
cs	Czech
cy	Welsh
da	Danish
de	German
el	Greek
en	English
es	Spanish
et	Estonian
fa	Persian
fil	Filipino
fi	Finnish
fr	French
gu	Gujarati
ha	Hausa
he	Hebrew
hi	Hindi
hu	Hungarian
hr	Croatian
ht	Haitian Creole
hy	Armenian
id	Indonesian
in	Obsolete; now replaced by `id`
is	Icelandic
it	Italian
iu	Inuktitut
iw	Obsolete; now replaced by `he`
ja	Japanese
ka	Georgian
kk	Kazakh
kn	Kannada
ko	Korean
lt	Lithuanian
lv	Latvian
ml	Malayalam
mr	Marathi
nb	Norwegian Bokmål
ne	Nepali
nl	Dutch; Flemish
no	Norwegian
pa	Panjabi
pl	Polish
ps	Pashto
pt	Portuguese
ro	Romanian
ru	Russian
sd	Sindhi
si	Sinhala
sk	Slovak
sl	Slovenian
sn	Shona
sr	Serbian
sv	Swedish
ta	Tamil
te	Telugu
th	Thai
tl	Tagalog
tr	Turkish
ug	Uyghur
uk	Ukrainian
und	Undefined
ur	Urdu
vi	Vietnamese
yo	Yoruba
zh	Chinese
zh-Hant	Chinese (Traditional)

Dataset Sources

Repository: https://huggingface.co/datasets/m2im/multilingual-twitter-collective-violence-dataset
Paper [optional]: TBD
Demo: The following code shows how to load and preview the dataset using the 🤗 datasets library:

from datasets import load_dataset

# Load the dataset from the Hugging Face Hub
dataset = load_dataset("m2im/multilingual-twitter-collective-violence-dataset")

# Check available splits
print(dataset)

# Preview the first few rows of the training set
dataset["train"].select(range(3))

Uses

Direct Use

This dataset is intended for training and evaluating machine learning models for:

Multilabel classification of tweets related to collective violence events
Early warning signal detection in social media
Temporal and geospatial conflict analysis

Out-of-Scope Use

This dataset should not be used to predict individual behavior or target specific persons. It is not appropriate for surveillance or activities that violate privacy or ethical use of social data.

Dataset Structure

Each row in the dataset includes:

tweetid: The tweet ID
lang: ISO 639-1 language code
40 multilabel indicators of the form {pre,post}{1,2,3,7}geo{10,20,30,50,70} indicating spatial and temporal proximity to a violent event.

Splits:

Train: 16,769,932 tweets
Validation: 4,192,483 tweets
Test: 2,329,158 tweets

Dataset Creation

Curation Rationale

To provide a robust multilingual dataset for modeling the connection between social media discourse and real-world collective violence, using a reproducible spatial-temporal labeling methodology.

Source Data

Data Collection and Processing

Tweets were extracted from a 10% historical Twitter archive licensed to NPS.
Events were taken from the UCDP GEDEvent_v22_1 dataset, filtered for geo- and temporal-precision.
A grid-based spatial indexing and geodesic distance computation was used to label tweets.

Who are the source data producers?

Tweets: Users of Twitter (varied demographics)
Events: Uppsala Conflict Data Program (academic researchers)

Annotations

No human annotations. Labels were generated using automated spatial-temporal heuristics.

Personal and Sensitive Information

Tweet text is not included in this dataset due to licensing restrictions that govern the ethical use of the Twitter archive for academic research. The dataset provides only tweet IDs, language tags (lang), and multilabel annotations. Users who wish to access the original tweet content must retrieve it directly from X using the tweetid field via the official X API.

Bias, Risks, and Limitations

Coverage bias: Only approximately 1% of tweets are geotagged; profile-based locations were used to improve coverage (~30%).
Event bias: Only events with high geospatial and temporal precision are included.
Language bias: Distribution depends on tweet language representation in the Twitter archive.

Recommendations

Consider language representation and spatiotemporal imbalance when training models.

Citation

@misc{mendieta2025multilingual,
  author       = {Milton Mendieta, Timothy Warren},
  title        = {Multilingual Twitter Dataset for Collective Violence Detection},
  year         = {2025},
  howpublished = {\url{https://huggingface.co/datasets/m2im/multilingual-twitter-collective-violence-dataset}},
  note         = {Dataset constructed using UCDP GEDEvent v22.1 and a licensed Twitter archive}
}

Glossary

Multilabel classification: Each instance (tweet) can have multiple labels simultaneously.
Spatial-temporal heuristics: Rules based on distance and time to label tweets relative to violent events.

More Information

For a detailed exploratory data analysis (EDA) of the dataset, please refer to the Jupyter notebook EDA_corpus.ipynb, available at https://github.com/m2im/violence_prediction/tree/main/Scripts

Dataset Card Authors

Dr. Milton Mendieta and Dr. Timothy Warren

Dataset Card Contact

[email protected]

Downloads last month: 88

Size of downloaded dataset files:

400 MB

Size of the auto-converted Parquet files:

400 MB

Number of rows:

23,291,573

Models trained or fine-tuned on m2im/multilingual-twitter-collective-violence-dataset