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Mixed Messages? The Limits of Automated Social Media Content Analysis
webThis CDT report examines limitations of NLP-based automated content analysis tools used for content moderation and law enforcement, directly relevant to AI safety concerns about biased or overbroad automated decision-making systems affecting free expression and civil liberties.
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Importance: 45/100organizational reportanalysis
Summary
Published by the Center for Democracy and Technology in 2017, this paper analyzes the capabilities and limitations of natural language processing tools used to analyze social media content for hate speech, terrorism, and other policy purposes. It identifies five key limitations of these tools and warns that over-reliance on them can lead to overbroad censorship and biased enforcement. The paper concludes with recommendations for policymakers and developers on evaluating automated content analysis tools.
Key Points
- •Automated NLP tools have limited ability to parse nuanced human communication or detect speaker intent and motivation at scale.
- •Policymakers often wrongly assume automated tools can replicate nuanced human analysis across large volumes of content.
- •Without proper safeguards, automated content analysis can facilitate overbroad censorship and biased enforcement of laws and platform policies.
- •The paper provides five specific limitations of NLP tools that caution against using them for critical determinations like immigration decisions.
- •Recommendations include a set of evaluative questions for policymakers assessing automated content analysis tools.
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Mixed Messages? The Limits of Automated Social Media Content Analysis - Center for Democracy and Technology
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AI Policy & Governance, Free Expression, Privacy & Data
Mixed Messages? The Limits of Automated Social Media Content Analysis
November 28, 2017
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Natasha Duarte, Emma Llansó
Governments and companies are turning to automated tools to make sense of what people post on social media, for everything ranging from hate speech detection to law enforcement investigations. Policymakers routinely call for social media companies to identify and take down hate speech, terrorist propaganda, harassment, “fake news” or disinformation, and other forms of problematic speech. Other policy proposals have focused on mining social media to inform law enforcement and immigration decisions. But these proposals wrongly assume that automated technology can accomplish on a large scale the kind of nuanced analysis that humans can accomplish on a small scale.
Today’s tools for automating social media content analysis have limited ability to parse the nuanced meaning of human communication, or to detect the intent or motivation of the speaker. Policymakers must understand these limitations before endorsing or adopting automated content analysis tools. Without proper safeguards, these tools can facilitate overbroad censorship and biased enforcement of laws and of platforms’ terms of service.
This paper explains the capabilities and limitations of tools for analyzing the text of social media posts and other online content. It is intended to help policymakers understand and evaluate available tools and the potential consequences of using them to carry out government policies. This paper focuses specifically on the use of natural language processi
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