Rights statement: The final publication is available at Springer via https://link.springer.com/article/10.1007/s12559-019-09695-3
Accepted author manuscript, 29.7 MB, PDF document
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Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - Analyzing Connections Between User Attributes, Images, and Text
AU - Burdick, Laura
AU - Mihalcea, Rada
AU - Boyd, Ryan
AU - Pennebaker, James W.
N1 - The final publication is available at Springer via https://link.springer.com/article/10.1007/s12559-019-09695-3
PY - 2021/3/23
Y1 - 2021/3/23
N2 - This work explores the relationship between a person’s demographic/ psychological traits (e.g., gender, personality) and selfidentity images and captions. We use a dataset of images and captions provided by N = 1,350 individuals, and we automatically extract features from both the images and captions. We identify several visual and textual properties that show reliable relationships with individual differences between participants. The automated techniques presented here allow us to draw interesting conclusions from our data that would be difficult to identify manually, and these techniques are extensible to other large datasets. We believe that our work on the relationship between user characteristics and user data has relevance in online settings, where users upload billions of images each day (Meeker M, 2014. Internet trends 2014–Code conference. Retrieved May 28, 2014).
AB - This work explores the relationship between a person’s demographic/ psychological traits (e.g., gender, personality) and selfidentity images and captions. We use a dataset of images and captions provided by N = 1,350 individuals, and we automatically extract features from both the images and captions. We identify several visual and textual properties that show reliable relationships with individual differences between participants. The automated techniques presented here allow us to draw interesting conclusions from our data that would be difficult to identify manually, and these techniques are extensible to other large datasets. We believe that our work on the relationship between user characteristics and user data has relevance in online settings, where users upload billions of images each day (Meeker M, 2014. Internet trends 2014–Code conference. Retrieved May 28, 2014).
KW - personality
KW - gender
KW - natural language processing
KW - computer vision
KW - computational social science
U2 - 10.1007/s12559-019-09695-3
DO - 10.1007/s12559-019-09695-3
M3 - Journal article
VL - 13
SP - 241
EP - 260
JO - Cognitive Computation
JF - Cognitive Computation
SN - 1866-9956
ER -