TY - GEN
T1 - Tweeting AI
T2 - 12th International AAAI Conference on Web and Social Media, ICWSM 2018
AU - Manikonda, Lydia
AU - Kambhampati, Subbarao
N1 - Funding Information:
Acknowledgements: This research is supported in part by a Google Faculty Research Award, an AFOSR grant FA9550-18-1-0067, the ONR grants N00014161-2892,N00014-13-1-0176,N00014-13-1-0519, N00014-15-1-2027, and the NASA grant NNX17AD06G.
Publisher Copyright:
Copyright © 2018, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
PY - 2018
Y1 - 2018
N2 - In light of the significant public interest in the AI technology and its impacts, in this research we set out to analyze the contours of public discourse and perceptions of AI, as reflected in the social media. We focus on Twitter, and analyze over two million AI related tweets posted by over 40,000 users. In addition to analyzing the macro characteristics of this whole discourse in terms of demographics, sentiment, and topics, we also provide a differential analysis of tweets from experts vs. non-experts, as well as a differential analysis of male vs. female tweeters. We see that (i) by and large the sentiments expressed in the AI discourse are more positive than is par for twitter (ii) that lay public tend to be more positive about AI than expert tweeters and (iii) that women tend to be more positive about AI impacts than men. Analysis of topics discussed also shows interesting differential patterns across experts vs. non-experts and men vs. women. For example, we see that women tend to focus more on the ethical issues surrounding AI. Our analysis provides a more nuanced picture of the public discourse on AI than can be gleaned from the media coverage.
AB - In light of the significant public interest in the AI technology and its impacts, in this research we set out to analyze the contours of public discourse and perceptions of AI, as reflected in the social media. We focus on Twitter, and analyze over two million AI related tweets posted by over 40,000 users. In addition to analyzing the macro characteristics of this whole discourse in terms of demographics, sentiment, and topics, we also provide a differential analysis of tweets from experts vs. non-experts, as well as a differential analysis of male vs. female tweeters. We see that (i) by and large the sentiments expressed in the AI discourse are more positive than is par for twitter (ii) that lay public tend to be more positive about AI than expert tweeters and (iii) that women tend to be more positive about AI impacts than men. Analysis of topics discussed also shows interesting differential patterns across experts vs. non-experts and men vs. women. For example, we see that women tend to focus more on the ethical issues surrounding AI. Our analysis provides a more nuanced picture of the public discourse on AI than can be gleaned from the media coverage.
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M3 - Conference contribution
AN - SCOPUS:85050640066
T3 - 12th International AAAI Conference on Web and Social Media, ICWSM 2018
SP - 652
EP - 655
BT - 12th International AAAI Conference on Web and Social Media, ICWSM 2018
PB - AAAI press
Y2 - 25 June 2018 through 28 June 2018
ER -