Measuring Asian Social Media Sentiments Toward Bat Exploitation

ABSTRACT

As human activities continue to negatively affect bat populations, bat conservation efforts continue to rely on questionnaires to understand human actions toward bats; however, the use of questionnaires constrains understanding by limiting the sample size to those who choose to participate, being subject to selection bias, and overall may not be the most efficient way of understanding sentiments and behaviors toward bats. We used social media to analyze sentiment toward bat exploitation behaviors in Asia and evaluated the influence that these posts have on users in the region. We gathered and analyzed a total of 458 social media posts and 2,427 comments throughout Asia utilizing keywords and hashtags in 16 languages. We found that nearly 90% of initial posts discussing bat exploitations were discussed in an acceptive, pro-bat exploitation way. Initial posts from Southeast and South Asia showed acceptance of bat exploitation. Comments on posts from Southeast Asia, particularly the Philippines and Indonesia, were acceptive of bat exploitation for food and medicine, whereas comments on posts from South Asia were rejective of bat exploitation, in contrast, with the initial South Asian posts, which were more acceptive of persecution of bats. We recommend using social media platforms to promote messages that reject bat exploitation and encourage bat conservation efforts as our results indicate that positive messages receive mostly positive comments, reinforcing the importance of protecting bats. Moreover, we suggest future work be conducted using social media to further understand region-specific narratives for and against bat exploitation.

KEYWORDS:

• Attitudes
• bats
• human–animal interaction
• sentiment analysis
• social media

Acknowledgements

We thank P. Barcas and the 2020–2022 Kingston Lab undergraduate research assistants for their contributions to early idea development and data collection.

Disclosure Statement

No potential conflict of interest was reported by the authors.

Data Availability Statement

Data at country and social media site level are available at https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi: 10.7910/DVN/V91XXK

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. Owing to the sensitive nature of these data, the full dataset is only available by request.

Use of Artificial Intelligence (AI)

During the preparation of this work the authors used Chat GPT in order to outline the conclusion. After using this tool/service, the authors reviewed and edited the content as needed and take full responsibility for the content of the publication.

Additional information

Funding

This work was supported by the National Science Foundation Graduate Research Fellowship Program (DGE 2140745) to ALR, and National Science Foundation AccelNet Award Number 2020595 to TK at Texas Tech University. Any opinions, findings, conclusions. or recommendations expressed in this work are those of the authors and do not necessarily reflect the views of the National Science Foundation.