TY - GEN
T1 - Anxiety and information seeking
T2 - 27th International World Wide Web, WWW 2018
AU - Youngmann, Brit
AU - Yom-Tov, Elad
N1 - Publisher Copyright:
© 2018 IW3C2 (International World Wide Web Conference Committee), published under Creative Commons CC BY 4.0 License.
PY - 2018/4/10
Y1 - 2018/4/10
N2 - People seeking information through search engines are assumed to behave similarly, regardless of the topic which they are searching. Here we use mouse tracking, which is correlated with gaze, to show that the information seeking patterns of people differ dramatically depending on their level of anxiety at the time of the search. We investigate the behavior of people during searches for medical symptoms, ranging from benign indications, where users are not usually anxious, to ones which could harbinger life-threatening conditions, where extreme anxiety is expected. We show that for the latter, 90% of people never saw more than the top 67% of the screen, compared to over 95% scanned by people seeking information on benign symptoms, even though relevant documents are similarly distributed in the results pages to these queries. Based on this observation, we develop a model which can predict the level of anxiety experienced by a user, using attributes derived from mouse tracking data and other user interactions. The model achieves Kendall's Tau of 0.48 with the medical severity of the symptoms searched. We show the importance of using information about the users? level of anxiety as predicted by the model, when measuring search engine performance. Our results prove that ignoring this information can lead to significant over-estimation of performance. Additionally, we show the utility of the model in three special instances: where multiple symptoms are searched concurrently; where the searcher has an underlying medical condition; and when users seek information on ways to commit suicide. In the latter, our results demonstrate the importance of help-line notices, and emphasize the need to measure the effective number of results seen by the user. Our results indicate that measures of relevance which use anxiety information can lead to more accurate understanding of the quality of search results, especially when delivering potentially life-saving information to users.
AB - People seeking information through search engines are assumed to behave similarly, regardless of the topic which they are searching. Here we use mouse tracking, which is correlated with gaze, to show that the information seeking patterns of people differ dramatically depending on their level of anxiety at the time of the search. We investigate the behavior of people during searches for medical symptoms, ranging from benign indications, where users are not usually anxious, to ones which could harbinger life-threatening conditions, where extreme anxiety is expected. We show that for the latter, 90% of people never saw more than the top 67% of the screen, compared to over 95% scanned by people seeking information on benign symptoms, even though relevant documents are similarly distributed in the results pages to these queries. Based on this observation, we develop a model which can predict the level of anxiety experienced by a user, using attributes derived from mouse tracking data and other user interactions. The model achieves Kendall's Tau of 0.48 with the medical severity of the symptoms searched. We show the importance of using information about the users? level of anxiety as predicted by the model, when measuring search engine performance. Our results prove that ignoring this information can lead to significant over-estimation of performance. Additionally, we show the utility of the model in three special instances: where multiple symptoms are searched concurrently; where the searcher has an underlying medical condition; and when users seek information on ways to commit suicide. In the latter, our results demonstrate the importance of help-line notices, and emphasize the need to measure the effective number of results seen by the user. Our results indicate that measures of relevance which use anxiety information can lead to more accurate understanding of the quality of search results, especially when delivering potentially life-saving information to users.
KW - Health
KW - Medicine.
KW - Mouse tracking
KW - Relevance
UR - http://www.scopus.com/inward/record.url?scp=85067096553&partnerID=8YFLogxK
U2 - 10.1145/3178876.3186156
DO - 10.1145/3178876.3186156
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AN - SCOPUS:85067096553
T3 - The Web Conference 2018 - Proceedings of the World Wide Web Conference, WWW 2018
SP - 753
EP - 762
BT - The Web Conference 2018 - Proceedings of the World Wide Web Conference, WWW 2018
PB - Association for Computing Machinery, Inc
Y2 - 23 April 2018 through 27 April 2018
ER -
