Short message communications: users, topics, and in-language processing

ABSTRACT

This paper investigates three dimensions of cross-domain analysis for humanitarian information processing: citizen reporting vs organizational reporting; Twitter vs SMS; and English vs non-English communications. Short messages sent during the response to the recent earthquake in Haiti and floods in Pakistan are analyzed. It is clear that SMS and Twitter were used very differently at the time, by different groups of people. SMS was primarily used by individuals on the ground while Twitter was primarily used by the international community. Turning to semi-automated strategies that employ natural language processing, it is found that English-optimal strategies do not carry over to Urdu or Kreyol, especially with regards to subword variation. Looking at machine-learning models that attempt to combine both Twitter and SMS, it is found that the cross-domain prediction accuracy is very poor, but some loss in accuracy can be overcome by learning prior distributions over the sources. It is concluded that there is only limited utility in treating SMS and Twitter as equivalent information sources -- perhaps much less than the relatively large number of recent Twitter-focused papers would indicate.

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