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- ItemChoosing your platform for social media drug research and improving your keyword filter list(2019) Adams, Nikki; Artigiani, Eleanor Erin; Wish, Eric D.Social media research often has two things in common: Twitter is the platform used and a keyword filter list is used to extract only relevant Tweets. Here we propose that (a) alternative platforms be considered more often when doing social media research, and (b) regardless of platform, researchers use word embeddings as a type of synonym discovery to improve their keyword filter list, both of which lead to more relevant data. We demonstrate the benefit of these proposals by comparing how successful our synonym discovery method is at finding terms for marijuana and select opioids on Twitter versus a platform that can be filtered by topic, Reddit. We also find words that are not on the U.S. Drug Enforcement Agency (DEA) drug slang list for that year, some of which appear on the list the subsequent year, showing that this method could be employed to find drug terms faster than traditional means.
- ItemFiltering Tweets for Social Unrest(IEEE, 2017-01) Mishler, Alan; Wonus, Kevin; Chambers, Wendy; Bloodgood, MichaelSince the events of the Arab Spring, there has been increased interest in using social media to anticipate social unrest. While efforts have been made toward automated unrest prediction, we focus on filtering the vast volume of tweets to identify tweets relevant to unrest, which can be provided to downstream users for further analysis. We train a supervised classifier that is able to label Arabic language tweets as relevant to unrest with high reliability. We examine the relationship between training data size and performance and investigate ways to optimize the model building process while minimizing cost. We also explore how confidence thresholds can be set to achieve desired levels of performance.
- ItemData Cleaning for XML Electronic Dictionaries via Statistical Anomaly Detection(IEEE, 2016) Bloodgood, Michael; Strauss, BenjaminMany important forms of data are stored digitally in XML format. Errors can occur in the textual content of the data in the fields of the XML. Fixing these errors manually is time-consuming and expensive, especially for large amounts of data. There is increasing interest in the research, development, and use of automated techniques for assisting with data cleaning. Electronic dictionaries are an important form of data frequently stored in XML format that frequently have errors introduced through a mixture of manual typographical entry errors and optical character recognition errors. In this paper we describe methods for flagging statistical anomalies as likely errors in electronic dictionaries stored in XML format. We describe six systems based on different sources of information. The systems detect errors using various signals in the data including uncommon characters, text length, character-based language models, word-based language models, tied-field length ratios, and tied-field transliteration models. Four of the systems detect errors based on expectations automatically inferred from content within elements of a single field type. We call these single-field systems. Two of the systems detect errors based on correspondence expectations automatically inferred from content within elements of multiple related field types. We call these tied-field systems. For each system, we provide an intuitive analysis of the type of error that it is successful at detecting. Finally, we describe two larger-scale evaluations using crowdsourcing with Amazon’s Mechanical Turk platform and using the annotations of a domain expert. The evaluations consistently show that the systems are useful for improving the efficiency with which errors in XML electronic dictionaries can be detected.
- ItemSupplemental Materials for Slevc, Davey, & Linck (2016), A new look at the 'hard problem' of bilingual lexical access: Evidence for language suppression with univalent stimuli(2015-10-14) Slevc, L. Robert; Davey, Nicholas; Linck, Jared A.This document provides the supporting documentation of the modeling procedure and interim results for the analyses reported in Slevc, Davey, and Linck (2016), A new look at "the hard problem" of bilingual lexical access: Evidence for language suppression with univalent stimuli.
- ItemA Method for Stopping Active Learning Based on Stabilizing Predictions and the Need for User-Adjustable Stopping(Association for Computational Linguistics, 2009-06) Bloodgood, Michael; Vijay-Shanker, KA survey of existing methods for stopping active learning (AL) reveals the needs for methods that are: more widely applicable; more aggressive in saving annotations; and more stable across changing datasets. A new method for stopping AL based on stabilizing predictions is presented that addresses these needs. Furthermore, stopping methods are required to handle a broad range of different annotation/performance tradeoff valuations. Despite this, the existing body of work is dominated by conservative methods with little (if any) attention paid to providing users with control over the behavior of stopping methods. The proposed method is shown to fill a gap in the level of aggressiveness available for stopping AL and supports providing users with control over stopping behavior.