2023
7.
Wicker, Jörg; Krauter, Nicolas; Derstorff, Bettina; Stönner, Christof; Bourtsoukidis, Efstratios; Klüpfel, Thomas; Williams, Jonathan; Kramer, Stefan
Cinema Experiments 2013 Miscellaneous
2023.
Links | BibTeX | Altmetric | PlumX | Tags: atmospheric chemistry, cinema data mining, data mining, machine learning, smell of fear, sof
@misc{Wicker2023cinema,
title = {Cinema Experiments 2013},
author = { J\"{o}rg Wicker and Nicolas Krauter and Bettina Derstorff and Christof St\"{o}nner and Efstratios Bourtsoukidis and Thomas Kl\"{u}pfel and Jonathan Williams and Stefan Kramer},
url = {https://auckland.figshare.com/articles/dataset/Cinema_Experiments_2013/22777364},
doi = {10.17608/k6.auckland.22777364.v3},
year = {2023},
date = {2023-05-23},
keywords = {atmospheric chemistry, cinema data mining, data mining, machine learning, smell of fear, sof},
pubstate = {published},
tppubtype = {misc}
}
6.
Stönner, Christof; Edtbauer, Achim; Derstorff, Bettina; Bourtsoukidis, Efstratios; Klüpfel, Thomas; Wicker, Jörg; Williams, Jonathan
Cinema Experiments 2015 Miscellaneous
2023.
Links | BibTeX | Altmetric | PlumX | Tags: cinema data mining, data mining, machine learning, smell of fear, sof
@misc{St\"{o}nner2023cinema,
title = {Cinema Experiments 2015},
author = { Christof St\"{o}nner and Achim Edtbauer and Bettina Derstorff and Efstratios Bourtsoukidis and Thomas Kl\"{u}pfel and J\"{o}rg Wicker and Jonathan Williams},
url = {https://auckland.figshare.com/articles/dataset/Cinema_Experiments_2015/22777352},
doi = {10.17608/k6.auckland.22777352.v2},
year = {2023},
date = {2023-05-23},
keywords = {cinema data mining, data mining, machine learning, smell of fear, sof},
pubstate = {published},
tppubtype = {misc}
}
5.
Bensemann, Joshua; Cheena, Hasnain; Huang, David Tse Jung; Broadbent, Elizabeth; Williams, Jonathan; Wicker, Jörg
From What You See to What We Smell: Linking Human Emotions to Bio-markers in Breath Journal Article
In: IEEE Transactions on Affective Computing, pp. 1-13, 2023, ISSN: 1949-3045.
Abstract | Links | BibTeX | Altmetric | PlumX | Tags: biomarkers, breath analysis, cheminformatics, cinema data mining, emotional response analysis, machine learning, smell of fear
@article{bensemann2023from,
title = {From What You See to What We Smell: Linking Human Emotions to Bio-markers in Breath},
author = {Joshua Bensemann and Hasnain Cheena and David Tse Jung Huang and Elizabeth Broadbent and Jonathan Williams and J\"{o}rg Wicker},
url = {https://ieeexplore.ieee.org/document/10123109
https://doi.org/10.17608/k6.auckland.22777364
https://doi.org/10.17608/k6.auckland.22777352 },
doi = {10.1109/TAFFC.2023.3275216},
issn = {1949-3045},
year = {2023},
date = {2023-05-11},
urldate = {2023-05-11},
journal = {IEEE Transactions on Affective Computing},
pages = {1-13},
abstract = {Research has shown that the composition of breath can differ based on the human’s behavioral patterns and mental and physical states immediately before being collected. These breath-collection techniques have also been extended to observe the general processes occurring in groups of humans and can link them to what those groups are collectively experiencing. In this research, we applied machine learning techniques to the breath data collected from cinema audiences. These techniques included XGBOOST Regression, Hierarchical Clustering, and Item Basket analyses created using the Apriori algorithm. They were conducted to find associations between the biomarkers in the crowd’s breath and the movie’s audio-visual stimuli and thematic events. This analysis enabled us to directly link what the group was experiencing and their biological response to that experience. We first extracted visual and auditory features from a movie to achieve this. We compared it to the biomarkers in the crowd’s breath using regression and pattern mining techniques. Our results supported the theory that a crowd’s collective experience directly correlates to the biomarkers in the crowd’s breath. Consequently, these findings suggest that visual and auditory experiences have predictable effects on the human
body that can be monitored without requiring expensive or invasive neuroimaging techniques.},
keywords = {biomarkers, breath analysis, cheminformatics, cinema data mining, emotional response analysis, machine learning, smell of fear},
pubstate = {published},
tppubtype = {article}
}
Research has shown that the composition of breath can differ based on the human’s behavioral patterns and mental and physical states immediately before being collected. These breath-collection techniques have also been extended to observe the general processes occurring in groups of humans and can link them to what those groups are collectively experiencing. In this research, we applied machine learning techniques to the breath data collected from cinema audiences. These techniques included XGBOOST Regression, Hierarchical Clustering, and Item Basket analyses created using the Apriori algorithm. They were conducted to find associations between the biomarkers in the crowd’s breath and the movie’s audio-visual stimuli and thematic events. This analysis enabled us to directly link what the group was experiencing and their biological response to that experience. We first extracted visual and auditory features from a movie to achieve this. We compared it to the biomarkers in the crowd’s breath using regression and pattern mining techniques. Our results supported the theory that a crowd’s collective experience directly correlates to the biomarkers in the crowd’s breath. Consequently, these findings suggest that visual and auditory experiences have predictable effects on the human
body that can be monitored without requiring expensive or invasive neuroimaging techniques.
body that can be monitored without requiring expensive or invasive neuroimaging techniques.
2019
4.
Williams, Jonathan; Stönner, Christof; Edtbauer, Achim; Derstorff, Bettina; Bourtsoukidis, Efstratios; Klüpfel, Thomas; Krauter, Nicolas; Wicker, Jörg; Kramer, Stefan
What can we learn from the air chemistry of crowds? Proceedings Article
In: Hansel, Armin; Dunkl, Jürgen (Ed.): 8th International Conference on Proton Transfer Reaction Mass Spectrometry and its Applications, pp. 121-123, Innsbruck University Press, Innsbruck, 2019.
Abstract | Links | BibTeX | Tags: atmospheric chemistry, breath analysis, cheminformatics, cinema data mining, data mining, emotional response analysis, machine learning, movie analysis, smell of fear, sof, time series
@inproceedings{williams2019what,
title = {What can we learn from the air chemistry of crowds?},
author = {Jonathan Williams and Christof St\"{o}nner and Achim Edtbauer and Bettina Derstorff and Efstratios Bourtsoukidis and Thomas Kl\"{u}pfel and Nicolas Krauter and J\"{o}rg Wicker and Stefan Kramer},
editor = {Armin Hansel and J\"{u}rgen Dunkl},
url = {https://www.ionicon.com/sites/default/files/uploads/doc/Contributions_8th-PTR-MS-Conference-2019_web.pdf#page=122},
year = {2019},
date = {2019-05-10},
booktitle = {8th International Conference on Proton Transfer Reaction Mass Spectrometry and its Applications},
pages = {121-123},
publisher = {Innsbruck University Press},
address = {Innsbruck},
abstract = {Current PTR-MS technology allows hundreds of volatile trace gases in air to be measured every second at extremely low levels (parts per trillion). These instruments are often used in atmospheric research on planes and ships and even in the Amazon rainforest. Recently, we have used this technology to examine air composition changes caused by large groups of people (10,000-30,000) under real world conditions at a football match and in a movie theater. In both cases the trace gas signatures measured in ambient air are shown to reflect crowd behavior. By applying advanced data mining techniques we have shown that groups of people reproducibly respond to certain emotional stimuli (e.g. suspense and comedy) by exhaling specific trace gases. Furthermore, we explore whether this information can be used to determine the age classification of films.},
keywords = {atmospheric chemistry, breath analysis, cheminformatics, cinema data mining, data mining, emotional response analysis, machine learning, movie analysis, smell of fear, sof, time series},
pubstate = {published},
tppubtype = {inproceedings}
}
Current PTR-MS technology allows hundreds of volatile trace gases in air to be measured every second at extremely low levels (parts per trillion). These instruments are often used in atmospheric research on planes and ships and even in the Amazon rainforest. Recently, we have used this technology to examine air composition changes caused by large groups of people (10,000-30,000) under real world conditions at a football match and in a movie theater. In both cases the trace gas signatures measured in ambient air are shown to reflect crowd behavior. By applying advanced data mining techniques we have shown that groups of people reproducibly respond to certain emotional stimuli (e.g. suspense and comedy) by exhaling specific trace gases. Furthermore, we explore whether this information can be used to determine the age classification of films.
2018
3.
Stönner, Christof; Edtbauer, Achim; Derstorff, Bettina; Bourtsoukidis, Efstratios; Klüpfel, Thomas; Wicker, Jörg; Williams, Jonathan
Proof of concept study: Testing human volatile organic compounds as tools for age classification of films Journal Article
In: PLOS One, vol. 13, no. 10, pp. 1-14, 2018.
Abstract | Links | BibTeX | Altmetric | PlumX | Tags: atmospheric chemistry, breath analysis, cheminformatics, cinema data mining, data mining, emotional response analysis, machine learning, movie analysis, smell of fear, sof, time series
@article{Stonner2018,
title = {Proof of concept study: Testing human volatile organic compounds as tools for age classification of films},
author = {Christof St\"{o}nner and Achim Edtbauer and Bettina Derstorff and Efstratios Bourtsoukidis and Thomas Kl\"{u}pfel and J\"{o}rg Wicker and Jonathan Williams},
doi = {10.1371/journal.pone.0203044},
year = {2018},
date = {2018-10-11},
journal = {PLOS One},
volume = {13},
number = {10},
pages = {1-14},
publisher = {Public Library of Science},
abstract = {Humans emit numerous volatile organic compounds (VOCs) through breath and skin. The nature and rate of these emissions are affected by various factors including emotional state. Previous measurements of VOCs and CO2 in a cinema have shown that certain chemicals are reproducibly emitted by audiences reacting to events in a particular film. Using data from films with various age classifications, we have studied the relationship between the emission of multiple VOCs and CO2 and the age classifier (0, 6, 12, and 16) with a view to developing a new chemically based and objective film classification method. We apply a random forest model built with time independent features extracted from the time series of every measured compound, and test predictive capability on subsets of all data. It was found that most compounds were not able to predict all age classifiers reliably, likely reflecting the fact that current classification is based on perceived sensibilities to many factors (e.g. incidences of violence, sex, antisocial behaviour, drug use, and bad language) rather than the visceral biological responses expressed in the data. However, promising results were found for isoprene which reliably predicted 0, 6 and 12 age classifiers for a variety of film genres and audience age groups. Therefore, isoprene emission per person might in future be a valuable aid to national classification boards, or even offer an alternative, objective, metric for rating films based on the reactions of large groups of people.},
keywords = {atmospheric chemistry, breath analysis, cheminformatics, cinema data mining, data mining, emotional response analysis, machine learning, movie analysis, smell of fear, sof, time series},
pubstate = {published},
tppubtype = {article}
}
Humans emit numerous volatile organic compounds (VOCs) through breath and skin. The nature and rate of these emissions are affected by various factors including emotional state. Previous measurements of VOCs and CO2 in a cinema have shown that certain chemicals are reproducibly emitted by audiences reacting to events in a particular film. Using data from films with various age classifications, we have studied the relationship between the emission of multiple VOCs and CO2 and the age classifier (0, 6, 12, and 16) with a view to developing a new chemically based and objective film classification method. We apply a random forest model built with time independent features extracted from the time series of every measured compound, and test predictive capability on subsets of all data. It was found that most compounds were not able to predict all age classifiers reliably, likely reflecting the fact that current classification is based on perceived sensibilities to many factors (e.g. incidences of violence, sex, antisocial behaviour, drug use, and bad language) rather than the visceral biological responses expressed in the data. However, promising results were found for isoprene which reliably predicted 0, 6 and 12 age classifiers for a variety of film genres and audience age groups. Therefore, isoprene emission per person might in future be a valuable aid to national classification boards, or even offer an alternative, objective, metric for rating films based on the reactions of large groups of people.
2.
Stönner, Christof; Edtbauer, Achim; Derstorff, Bettina; Bourtsoukidis, Efstratios; Klüpfel, Thomas; Wicker, Jörg; Williams, Jonathan
Investigating human emissions of volatile organic compounds in a cinema, flux rates, links to scene content, and possible applications Conference
15th Conference of the International Society of Indoor Air Quality and Climate, INDOOR AIR 2018, International Society of Indoor Air Quality and Climate, 2018, ISBN: 978-171382651-4.
Abstract | BibTeX | Tags: atmospheric chemistry, cheminformatics, cinema data mining, sof
@conference{St\"{o}nner2018investigating,
title = {Investigating human emissions of volatile organic compounds in a cinema, flux rates, links to scene content, and possible applications},
author = {Christof St\"{o}nner and Achim Edtbauer and Bettina Derstorff and Efstratios Bourtsoukidis and Thomas Kl\"{u}pfel and J\"{o}rg Wicker and Jonathan Williams},
isbn = {978-171382651-4},
year = {2018},
date = {2018-07-22},
booktitle = {15th Conference of the International Society of Indoor Air Quality and Climate, INDOOR AIR 2018},
publisher = {International Society of Indoor Air Quality and Climate},
abstract = {Humans emit numerous volatile organic compounds (VOCs) into the air via skin and breath. These emissions can depend on various factors such as nutrition, sporting activity and also the emotional state. It is shown that the emission rates of the main endogenous breath gases like CO2, acetone and isoprene are generally lower for children than for adults. In contrast, VOCs from exogenous sources strongly vary over the course of day. Interestingly, small scale variances in emission rates were found to occur reproducibly over multiple screenings of the same film. The peaks occurring in the time series of a compound during the screening of the film were induced by the physiological response of the audience to audio-visual stimuli. Additionally, the question whether this chemical reaction of the audience can be used for the prediction of age classification of films is addressed.},
keywords = {atmospheric chemistry, cheminformatics, cinema data mining, sof},
pubstate = {published},
tppubtype = {conference}
}
Humans emit numerous volatile organic compounds (VOCs) into the air via skin and breath. These emissions can depend on various factors such as nutrition, sporting activity and also the emotional state. It is shown that the emission rates of the main endogenous breath gases like CO2, acetone and isoprene are generally lower for children than for adults. In contrast, VOCs from exogenous sources strongly vary over the course of day. Interestingly, small scale variances in emission rates were found to occur reproducibly over multiple screenings of the same film. The peaks occurring in the time series of a compound during the screening of the film were induced by the physiological response of the audience to audio-visual stimuli. Additionally, the question whether this chemical reaction of the audience can be used for the prediction of age classification of films is addressed.
2015
1.
Wicker, Jörg; Krauter, Nicolas; Derstorff, Bettina; Stönner, Christof; Bourtsoukidis, Efstratios; Klüpfel, Thomas; Williams, Jonathan; Kramer, Stefan
Cinema Data Mining: The Smell of Fear Proceedings Article
In: Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1235-1304, ACM ACM, New York, NY, USA, 2015, ISBN: 978-1-4503-3664-2.
Abstract | Links | BibTeX | Altmetric | PlumX | Tags: atmospheric chemistry, breath analysis, causality, cheminformatics, cinema data mining, data mining, emotional response analysis, movie analysis, smell of fear, sof, time series
@inproceedings{wicker2015cinema,
title = {Cinema Data Mining: The Smell of Fear},
author = {J\"{o}rg Wicker and Nicolas Krauter and Bettina Derstorff and Christof St\"{o}nner and Efstratios Bourtsoukidis and Thomas Kl\"{u}pfel and Jonathan Williams and Stefan Kramer},
url = {https://wicker.nz/nwp-acm/authorize.php?id=N10031
http://doi.acm.org/10.1145/2783258.2783404},
doi = {10.1145/2783258.2783404},
isbn = {978-1-4503-3664-2},
year = {2015},
date = {2015-01-01},
booktitle = {Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1235-1304},
publisher = {ACM},
address = {New York, NY, USA},
organization = {ACM},
series = {KDD '15},
abstract = {While the physiological response of humans to emotional events or stimuli is well-investigated for many modalities (like EEG, skin resistance, ...), surprisingly little is known about the exhalation of so-called Volatile Organic Compounds (VOCs) at quite low concentrations in response to such stimuli. VOCs are molecules of relatively small mass that quickly evaporate or sublimate and can be detected in the air that surrounds us. The paper introduces a new field of application for data mining, where trace gas responses of people reacting on-line to films shown in cinemas (or movie theaters) are related to the semantic content of the films themselves. To do so, we measured the VOCs from a movie theatre over a whole month in intervals of thirty seconds, and annotated the screened films by a controlled vocabulary compiled from multiple sources. To gain a better understanding of the data and to reveal unknown relationships, we have built prediction models for so-called forward prediction (the prediction of future VOCs from the past), backward prediction (the prediction of past scene labels from future VOCs) and for some forms of abductive reasoning and Granger causality. Experimental results show that some VOCs and some labels can be predicted with relatively low error, and that hints for causality with low p-values can be detected in the data.},
keywords = {atmospheric chemistry, breath analysis, causality, cheminformatics, cinema data mining, data mining, emotional response analysis, movie analysis, smell of fear, sof, time series},
pubstate = {published},
tppubtype = {inproceedings}
}
While the physiological response of humans to emotional events or stimuli is well-investigated for many modalities (like EEG, skin resistance, …), surprisingly little is known about the exhalation of so-called Volatile Organic Compounds (VOCs) at quite low concentrations in response to such stimuli. VOCs are molecules of relatively small mass that quickly evaporate or sublimate and can be detected in the air that surrounds us. The paper introduces a new field of application for data mining, where trace gas responses of people reacting on-line to films shown in cinemas (or movie theaters) are related to the semantic content of the films themselves. To do so, we measured the VOCs from a movie theatre over a whole month in intervals of thirty seconds, and annotated the screened films by a controlled vocabulary compiled from multiple sources. To gain a better understanding of the data and to reveal unknown relationships, we have built prediction models for so-called forward prediction (the prediction of future VOCs from the past), backward prediction (the prediction of past scene labels from future VOCs) and for some forms of abductive reasoning and Granger causality. Experimental results show that some VOCs and some labels can be predicted with relatively low error, and that hints for causality with low p-values can be detected in the data.