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Effective social post classifiers on top of search interfaces

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Abstract

Applying text classification to find social media posts relevant to a topic of interest is the focus of a substantial amount of research. A key challenge is how to select a good training set of posts to label. This problem has traditionally been solved using active learning. However, this assumes access to all posts of the collection, which is not realistic in many cases, as social networks impose constraints on the number of posts that can be retrieved through their search APIs. To address this problem, which we refer as the training post retrieval over constrained search interfaces problem, we propose several keyword selection algorithms that, given a topic, generate an effective set of keyword queries to submit to the search API. The returned posts are labeled and used as a training dataset to train post classifiers. Our experiments compare our proposed keyword selection algorithms to several baselines across various topics from three sources. The results show that the proposed methods generate superior training sets, which is measured by the balanced accuracy of the trained classifiers.

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Data Availability Statement

The datasets used in our experiments were collected from DailyStrength, Reddit, and Misra (2018).

Notes

  1. This value was used by the experiments in Wang et al. (2016).

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Funding

This work was supported by NSF Grants IIS-1838222 and IIS-1901379.

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Authors and Affiliations

  1. University of California, Riverside, Riverside, CA, USA

    Ryan Rivas & Vagelis Hristidis

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  1. Ryan Rivas
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  2. Vagelis Hristidis
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Correspondence to Ryan Rivas.

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Code availability

The code used in our experiments is available from: https://github.com/rriva002/Training-Post-Retrieval.

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Responsible editor: Annalisa Appice, Sergio Escalera, Jose A. Gamez, Heike Trautmann

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Rivas, R., Hristidis, V. Effective social post classifiers on top of search interfaces. Data Min Knowl Disc 35, 1809–1829 (2021). https://doi.org/10.1007/s10618-021-00768-2

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