{'fraction_non_alphanumeric': 0.025728987993138937, 'fraction_numerical': 0.0, 'mean_word_length': 5.556179775280899, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Emergency-relief coordination on social media: Automatically matching resource requests and offers

Disaster affected communities are increasingly turning to social media for communication and coordination. This includes reports on needs (demands) and offers (supplies) of resources required during emergency situations. Identifying and matching such requests with potential responders can substantially accelerate emergency relief efforts. Current work of disaster management agencies is labor intensive, and there is substantial interest in automated tools.We present machine–learning methods to automatically identify and match needs and offers communicated via social media for items and services such as shelter, money, clothing, etc. For instance, a message such as “we are coordinating a clothing/food drive for families affected by Hurricane Sandy. If you would like to donate, DM us” can be matched with a message such as “I got a bunch of clothes I’d like to donate to hurricane sandy victims. Anyone know where/how I can do that?” Compared to traditional search, our results can significantly improve the matchmaking efforts of disaster response agencies.