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A Scalable and Automated Framework for Tracking the likely Adoption of Emerging Technologies

Lowri Williams, Eirini Anthi, Pete Burnap

TL;DR

The paper addresses the need for scalable understanding of emerging-tech adoption barriers and opportunities by leveraging a large-scale Twitter corpus and an automated aspect-based sentiment analysis pipeline. It combines direct CyBOK-based aspect extraction with VADER sentiment scoring, providing time-resolved, per-aspect sentiment to infer adoption likelihood. Ground-truth evaluation against human annotators yields strong agreement ($\alpha = 0.769$, about 82% overall), supporting the reliability of automated signals for decision-support. The work enables sector-customizable, data-driven insights into adoption dynamics and identifies negative sentiment drivers (e.g., misinformation, cybersecurity) alongside positive adoption signals, with practical implications for risk assessment and strategy.

Abstract

While new technologies are expected to revolutionise and become game-changers in improving the efficiencies and practises of our daily lives, it is also critical to investigate and understand the barriers and opportunities faced by their adopters. Such findings can serve as an additional feature in the decision-making process when analysing the risks, costs, and benefits of adopting an emerging technology in a particular setting. Although several studies have attempted to perform such investigations, these approaches adopt a qualitative data collection methodology which is limited in terms of the size of the targeted participant group and is associated with a significant manual overhead when transcribing and inferring results. This paper presents a scalable and automated framework for tracking likely adoption and/or rejection of new technologies from a large landscape of adopters. In particular, a large corpus of social media texts containing references to emerging technologies was compiled. Text mining techniques were applied to extract sentiments expressed towards technology aspects. In the context of the problem definition herein, we hypothesise that the expression of positive sentiment infers an increase in the likelihood of impacting a technology user's acceptance to adopt, integrate, and/or use the technology, and negative sentiment infers an increase in the likelihood of impacting the rejection of emerging technologies by adopters. To quantitatively test our hypothesis, a ground truth analysis was performed to validate that the sentiment captured by the text mining approach is comparable to the results given by human annotators when asked to label whether such texts positively or negatively impact their outlook towards adopting an emerging technology.

A Scalable and Automated Framework for Tracking the likely Adoption of Emerging Technologies

TL;DR

The paper addresses the need for scalable understanding of emerging-tech adoption barriers and opportunities by leveraging a large-scale Twitter corpus and an automated aspect-based sentiment analysis pipeline. It combines direct CyBOK-based aspect extraction with VADER sentiment scoring, providing time-resolved, per-aspect sentiment to infer adoption likelihood. Ground-truth evaluation against human annotators yields strong agreement (, about 82% overall), supporting the reliability of automated signals for decision-support. The work enables sector-customizable, data-driven insights into adoption dynamics and identifies negative sentiment drivers (e.g., misinformation, cybersecurity) alongside positive adoption signals, with practical implications for risk assessment and strategy.

Abstract

While new technologies are expected to revolutionise and become game-changers in improving the efficiencies and practises of our daily lives, it is also critical to investigate and understand the barriers and opportunities faced by their adopters. Such findings can serve as an additional feature in the decision-making process when analysing the risks, costs, and benefits of adopting an emerging technology in a particular setting. Although several studies have attempted to perform such investigations, these approaches adopt a qualitative data collection methodology which is limited in terms of the size of the targeted participant group and is associated with a significant manual overhead when transcribing and inferring results. This paper presents a scalable and automated framework for tracking likely adoption and/or rejection of new technologies from a large landscape of adopters. In particular, a large corpus of social media texts containing references to emerging technologies was compiled. Text mining techniques were applied to extract sentiments expressed towards technology aspects. In the context of the problem definition herein, we hypothesise that the expression of positive sentiment infers an increase in the likelihood of impacting a technology user's acceptance to adopt, integrate, and/or use the technology, and negative sentiment infers an increase in the likelihood of impacting the rejection of emerging technologies by adopters. To quantitatively test our hypothesis, a ground truth analysis was performed to validate that the sentiment captured by the text mining approach is comparable to the results given by human annotators when asked to label whether such texts positively or negatively impact their outlook towards adopting an emerging technology.
Paper Structure (10 sections, 4 figures, 2 tables)

This paper contains 10 sections, 4 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Data Collection and Preparation
  4. Aspect-Based Sentiment Analysis
  5. Aspect Extraction
  6. Sentiment Analysis
  7. Results and Discussion
  8. Evaluation
  9. Conclusion
  10. Future Work

Figures (4)

  • Figure 1: Top 30 most frequently referenced CyBOK terms used across the dataset
  • Figure 2: Chronological aspect-based analysis results across an excerpt of emerging technologies from the whole dataset (positive sentiment = green, neutral sentiment = orange, negative sentiment = red)
  • Figure 3: Chronological aspect-based analysis results across an excerpt of emerging technologies from discourse relating to healthcare (positive sentiment = green, neutral sentiment = orange, negative sentiment = red)
  • Figure 4: Chronological aspect-based analysis results across an excerpt of emerging technologies from discourse relating to education (positive sentiment = green, neutral sentiment = orange, negative sentiment = red)