{'fraction_non_alphanumeric': 0.014964788732394365, 'fraction_numerical': 0.0, 'mean_word_length': 4.742424242424242, '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}}
Routing Questions in Twitter: An Effective Way to Qualify Peer Helpers

Essentially social query consists in sharing a question on a social network and waiting for responses. The usual strategy of sharing a public question is inefficient because it does not ensure an answer, either quality or agility. We assume that directing the question to a specific user is the best way to get a right and quick answer. However, choosing this person could be a hard task and by choosing a wrong one we could receive an incorrect answer or the person might just ignore the question. In this paper, we proposed, implemented and evaluated a model based on a multi-criteria approach to find the most suitable user to receive a question on Twitter. We rank expert candidates using the Weight Product Model. One of the main differentials of our model is the use of multi-criteria and that it was built for a social network context. In the evaluation, using as metrics the Normalized Discounted Cumulative Gain and the Spearman Rank Correlation Coefficient, our technique achieved promising results in predicting the usefulness of real responders answer.