Public Perception of the Brain-Computer Interface Based on a Decade of Data on X: Mixed Methods Study
Abstract BackgroundGiven the recent evolution and achievements in brain-computer interface (BCI) technologies, understanding public perception and sentiments toward such novel technologies is important for guiding their communication strategies in marketing and education....
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| Format: | Article |
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JMIR Publications
2025-06-01
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| Series: | JMIR Formative Research |
| Online Access: | https://formative.jmir.org/2025/1/e60859 |
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| author | Mohammed A Almanna Lior M Elkaim Mohammed A Alvi Jordan J Levett Ben Li Muhammad Mamdani Mohammed Al‑Omran Naif M Alotaibi |
| author_facet | Mohammed A Almanna Lior M Elkaim Mohammed A Alvi Jordan J Levett Ben Li Muhammad Mamdani Mohammed Al‑Omran Naif M Alotaibi |
| author_sort | Mohammed A Almanna |
| collection | DOAJ |
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Abstract
BackgroundGiven the recent evolution and achievements in brain-computer interface (BCI) technologies, understanding public perception and sentiments toward such novel technologies is important for guiding their communication strategies in marketing and education.
ObjectiveThis study aims to explore the public perception of BCI technology by examining posts on X (formerly known as Twitter) using natural language processing (NLP) methods.
MethodsA mixed methods study was conducted on BCI-related posts from January 2010 to December 2021. The dataset included 65,340 posts from 38,962 unique users. This dataset was subject to a detailed NLP analysis including VADER, TextBlob, and NRCLex libraries, focusing on quantifying the sentiment (positive, neutral, and negative), the degree of subjectivity, and the range of emotions expressed in the posts. The temporal dynamics of sentiments were examined using the Mann-Kendall trend test to identify significant trends or shifts in public interest over time, based on monthly incidence. We used the Sentiment.ai tool to infer users’ demographics by matching predefined attributes in users’ profile biographies to certain demographic groups. We used the BERTopic tool for semantic understanding of discussions related to BCI.
ResultsThe analysis showed a significant rise in BCI discussions in 2017, coinciding with Elon Musk’s announcement of Neuralink. Sentiment analysis revealed that 59.38% (38,804/65,340) of posts were neutral, 32.75% (21,404/65,340) were positive, and 7.85% (5132/65,340) were negative. The average polarity score demonstrated a generally positive trend over the course of the study (Mann-Kendall Statistic=0.266; τ=0.266; P
ConclusionsThis NLP-assisted study provides a decade-long analysis of public perception of BCI technology based on data from X. Overall, sentiments were neutral yet cautiously apprehensive, with anticipation, trust, and fear as the dominant emotions. The presence of fear underscores the need to address ethical concerns, particularly around data privacy, safety, and transparency. Transparent communication and ethical considerations are essential for building public trust and reducing apprehension. Influential figures and positive clinical outcomes, such as advancements in neuroprosthetics, could enhance favorable perceptions. The gamification of BCI, particularly in gaming and entertainment, also offers potential for wider public engagement and adoption. However, public perceptions on X may differ from other platforms, affecting the broader interpretation of results. Despite these limitations, the findings provide valuable insights for guiding future BCI developments, policy making, and communication strategies. |
| format | Article |
| id | doaj-art-2fa1109e057e472db1d273f475d6b8ec |
| institution | Kabale University |
| issn | 2561-326X |
| language | English |
| publishDate | 2025-06-01 |
| publisher | JMIR Publications |
| record_format | Article |
| series | JMIR Formative Research |
| spelling | doaj-art-2fa1109e057e472db1d273f475d6b8ec2025-08-20T03:30:40ZengJMIR PublicationsJMIR Formative Research2561-326X2025-06-019e60859e6085910.2196/60859Public Perception of the Brain-Computer Interface Based on a Decade of Data on X: Mixed Methods StudyMohammed A Almannahttp://orcid.org/0009-0007-0371-7620Lior M Elkaimhttp://orcid.org/0000-0001-7500-8398Mohammed A Alvihttp://orcid.org/0000-0002-7131-079XJordan J Levetthttp://orcid.org/0000-0002-1999-2017Ben Lihttp://orcid.org/0000-0002-7191-1034Muhammad Mamdanihttp://orcid.org/0000-0001-5199-6344Mohammed Al‑Omranhttp://orcid.org/0000-0003-3325-0420Naif M Alotaibihttp://orcid.org/0000-0002-8227-347X Abstract BackgroundGiven the recent evolution and achievements in brain-computer interface (BCI) technologies, understanding public perception and sentiments toward such novel technologies is important for guiding their communication strategies in marketing and education. ObjectiveThis study aims to explore the public perception of BCI technology by examining posts on X (formerly known as Twitter) using natural language processing (NLP) methods. MethodsA mixed methods study was conducted on BCI-related posts from January 2010 to December 2021. The dataset included 65,340 posts from 38,962 unique users. This dataset was subject to a detailed NLP analysis including VADER, TextBlob, and NRCLex libraries, focusing on quantifying the sentiment (positive, neutral, and negative), the degree of subjectivity, and the range of emotions expressed in the posts. The temporal dynamics of sentiments were examined using the Mann-Kendall trend test to identify significant trends or shifts in public interest over time, based on monthly incidence. We used the Sentiment.ai tool to infer users’ demographics by matching predefined attributes in users’ profile biographies to certain demographic groups. We used the BERTopic tool for semantic understanding of discussions related to BCI. ResultsThe analysis showed a significant rise in BCI discussions in 2017, coinciding with Elon Musk’s announcement of Neuralink. Sentiment analysis revealed that 59.38% (38,804/65,340) of posts were neutral, 32.75% (21,404/65,340) were positive, and 7.85% (5132/65,340) were negative. The average polarity score demonstrated a generally positive trend over the course of the study (Mann-Kendall Statistic=0.266; τ=0.266; P ConclusionsThis NLP-assisted study provides a decade-long analysis of public perception of BCI technology based on data from X. Overall, sentiments were neutral yet cautiously apprehensive, with anticipation, trust, and fear as the dominant emotions. The presence of fear underscores the need to address ethical concerns, particularly around data privacy, safety, and transparency. Transparent communication and ethical considerations are essential for building public trust and reducing apprehension. Influential figures and positive clinical outcomes, such as advancements in neuroprosthetics, could enhance favorable perceptions. The gamification of BCI, particularly in gaming and entertainment, also offers potential for wider public engagement and adoption. However, public perceptions on X may differ from other platforms, affecting the broader interpretation of results. Despite these limitations, the findings provide valuable insights for guiding future BCI developments, policy making, and communication strategies.https://formative.jmir.org/2025/1/e60859 |
| spellingShingle | Mohammed A Almanna Lior M Elkaim Mohammed A Alvi Jordan J Levett Ben Li Muhammad Mamdani Mohammed Al‑Omran Naif M Alotaibi Public Perception of the Brain-Computer Interface Based on a Decade of Data on X: Mixed Methods Study JMIR Formative Research |
| title | Public Perception of the Brain-Computer Interface Based on a Decade of Data on X: Mixed Methods Study |
| title_full | Public Perception of the Brain-Computer Interface Based on a Decade of Data on X: Mixed Methods Study |
| title_fullStr | Public Perception of the Brain-Computer Interface Based on a Decade of Data on X: Mixed Methods Study |
| title_full_unstemmed | Public Perception of the Brain-Computer Interface Based on a Decade of Data on X: Mixed Methods Study |
| title_short | Public Perception of the Brain-Computer Interface Based on a Decade of Data on X: Mixed Methods Study |
| title_sort | public perception of the brain computer interface based on a decade of data on x mixed methods study |
| url | https://formative.jmir.org/2025/1/e60859 |
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