Question: What does the author attempt to experiment with to answer the two research questions?
Evidence: Micro-blogging social media platforms have become very popular in recent years. One of the most popular platforms is Twitter, which allows users to broadcast short texts (i.e., 140 characters initially, and 280 characters in a recent platform update) in real time with almost no restrictions on content. Twitter is a source of people’s attitudes, opinions, and thoughts toward the things that happen in their daily life. Twitter data are publicly accessible through Twitter application programming interface (API); and there are several tools to download and process these data. Twitter is being increasingly used as a valuable instrument for surveillance research and predictive analytics in many fields including epidemiology, psychology, and social sciences. For example, Bian et al. explored the relation between promotional information and laypeople’s discussion on Twitter by using topic modeling and sentiment analysis BIBREF0. Zhao et al. assessed the mental health signals among sexual and gender minorities using Twitter data BIBREF1. Twitter data can be used to study and predict population-level targets, such as disease incidence BIBREF2, political trends BIBREF3, earthquake detection BIBREF4, and crime perdition BIBREF5, and individual-level outcomes or life events, such as job loss BIBREF6, depression BIBREF7, and adverse events BIBREF8. Since tweets are unstructured textual data, natural language processing (NLP) and machine learning, especially deep learning nowadays, are often used for preprocessing and analytics. However, for many studiesBIBREF9, BIBREF10, BIBREF11, especially those that analyze individual-level targets, manual annotations of several thousands of tweets, often by experts, is needed to create gold-standard training datasets, to be fed to the NLP and machine learning tools for subsequent, reliable automated processing of millions of tweets. Manual annotation is obviously labor intense and time consuming.
Crowdsourcing can scale up manual labor by distributing tasks to a large set of workers working in parallel instead of a single people working serially BIBREF12. Commercial platforms such as Amazon’s Mechanical Turk (MTurk, https://www.
mturk.com/), make it easy to recruit a large crowd of people working remotely to perform time consuming manual tasks such as entity resolution BIBREF13, BIBREF14, image or sentiment annotation BIBREF15, BIBREF16. The annotation tasks published on MTurk can be done on a piecework basis and, given the very large pool of workers usually available (even by selecting a subset of those who have, say, a college degree), the tasks can be done almost immediately. However, any crowdsourcing service that solely relies on human workers will eventually be expensive when large datasets are needed, that is often the case when creating training datasets for NLP and deep learning tasks. Therefore, reducing the training dataset size (without losing performance and quality) would also improve efficiency while contain costs.
Query optimization techniques (e.g., active learning) can reduce the number of tweets that need to be labeled, while yielding comparable performance for the downstream machine learning tasks BIBREF17, BIBREF18, BIBREF19. Active learning algorithms have been widely applied in various areas including NLP BIBREF20 and image processing BIBREF21. In a pool-based active learning scenario, data samples for training a machine learning algorithm (e.g., a classifier for identifying job loss events) are drawn from a pool of unlabeled data according to some forms of informativeness measure (a.k.a. active learning strategies BIBREF22), and then the most informative instances are selected to be annotated. For a classification task, in essence, an active learning strategy should be able to pick the “best” samples to be labelled that will improve the classification performance the most.
In this study, we integrated active learning into a crowdsourcing pipeline for the classification of life events based on individual tweets. We analyzed the quality of crowdsourcing annotations and then experimented with different machine/deep learning classifiers combined with different active learning strategies to answer the following two research questions (RQs):
RQ1. How does (1) the amount of time that a human worker spends on and (2) the number of workers assigned to each annotation task impact the quality of an-notation results?
RQ2. Which active learning strategy is most efficient and cost-effective to build event classification models using Twitter data?
-5pt
We analyzed the quality of crowdsourcing annotations and then experimented with different machine/deep learning classifiers combined with different active learning strategies to answer the following two research questions (RQs): RQ1. How does (1) the amount of time that a human worker spends on and (2) the number of workers assigned to each annotation task impact the quality of an-notation results? RQ2. Which active learning strategy is most efficient and cost-effective to build event classification models using Twitter data?
Answer: Different active learning strategies.