@inproceedings{12bd3e40a3ad4f68a482276cb0c44144,
title = "CrowdQM: Learning Aspect-Level User Reliability and Comment Trustworthiness in Discussion Forums",
abstract = "Community discussion forums are increasingly used to seek advice; however, they often contain conflicting and unreliable information. Truth discovery models estimate source reliability and infer information trustworthiness simultaneously in a mutual reinforcement manner, and can be used to distinguish trustworthy comments with no supervision. However, they do not capture the diversity of word expressions and learn a single reliability score for the user. CrowdQM addresses these limitations by modeling the fine-grained aspect-level reliability of users and incorporate semantic similarity between words to learn a latent trustworthy comment embedding. We apply our latent trustworthy comment for comment ranking for three diverse communities in Reddit and show consistent improvement over non-aspect based approaches. We also show qualitative results on learned reliability scores and word embeddings by our model.",
author = "Alex Morales and Kanika Narang and Hari Sundaram and Chengxiang Zhai",
note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2020.; 24th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2020 ; Conference date: 11-05-2020 Through 14-05-2020",
year = "2020",
doi = "10.1007/978-3-030-47426-3_46",
language = "English (US)",
isbn = "9783030474256",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
pages = "592--605",
editor = "Lauw, {Hady W.} and Ee-Peng Lim and Wong, {Raymond Chi-Wing} and Alexandros Ntoulas and See-Kiong Ng and Pan, {Sinno Jialin}",
booktitle = "Advances in Knowledge Discovery and Data Mining - 24th Pacific-Asia Conference, PAKDD 2020, Proceedings",
address = "Germany",
}