TY - JOUR AU - Chen, Jilin AU - Cypher, Allen AU - Drews, Clemens AU - Nichols, Jeffrey PY - 2021/08/03 Y2 - 2024/03/28 TI - CrowdE: Filtering Tweets for Direct Customer Engagements JF - Proceedings of the International AAAI Conference on Web and Social Media JA - ICWSM VL - 7 IS - 1 SE - Full Papers DO - 10.1609/icwsm.v7i1.14378 UR - https://ojs.aaai.org/index.php/ICWSM/article/view/14378 SP - 51-60 AB - <p> Many consumer brands have customer relationship agents that directly engage opinionated consumers on social streams, such as Twitter. To help agents find opinionated consumers, social stream monitoring tools provide keyword-based filters, which are often too coarse-grained to be effective. In this work, we introduce CrowdE, a Twitter-based filtering system that helps agents find opinionated customers through brand-specific intelligent filters. To minimize per-brand effort in creating these brand-specific filters, the system used a common crowd-enabled process that creates the filters through machine learning over crowd-labeled tweets. We validated the quality of the crowd labels and the performance of the filter algorithms built from the labels. A user evaluation further showed that CrowdE's intelligent filters improved task performance and were generally preferred by users in comparison to keyword-based filters in current social stream monitoring tools. </p> ER -