Engelbart (2020)
article⁄Engelbart (2020)
abstract⁄The internet has long been viewed as a cyberspace of free and collective information, allowing for an increase in the diversity of ideas and viewpoints available to the general public. However, critics argue that the emergence of personalization algorithms on social media and other internet platforms instead reduces information diversity by forming ‘filter bubbles’’ of viewpoints similar to the user’s own. The adoption of these personalization algorithms is due in part to advancements in natural language processing, which allow for textual analysis at unprecedented scales. This paper aims to utilize natural language processing and architectural spatial principles to present social media from a collective viewpoint rather than a personalized one. To accomplish this, the paper introduces Engelbart, a datadriven agentbased system, where realtime Twitter conversations are visualized within a twodimensional environment. This environment is interacted with by the artificial intelligence AI agent, Engelbart, which summarizes crowdsourced thoughts and feelings about current trending topics. The functionality of this web application comes from the natural language processing of thousands of tweets per minute throughout several layers of operations, including sentiment analysis and word embeddings. Presented as an understandable interface, it incorporates the values of cybernetics, cyberspace, agentbased modeling, and data ethics to show the potential for social media to become a more transparent space for collective discussion.
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| Year |
2020 |
| Authors |
Duong, Eric; Vercoe, Garrett; Baharlou, Ehsan. |
| Issue |
ACADIA 2020: Distributed Proximities / Volume I: Technical Papers |
| Pages |
406-415. |
| Library link |
N/A |
| Entry filename |
engelbart |
