BU-NEmo: an affective dataset of gun violence news

Carley Reardon; Sejin Paik; Ge Gao; Meet Parekh; Yanling Zhao; Lei Guo; Margrit Betke; Derry Wijaya

BU-NEmo: an affective dataset of gun violence news

Carley Reardon, Sejin Paik, Ge Gao, Meet Parekh, Yanling Zhao, Lei Guo, Margrit Betke, Derry Wijaya

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

Abstract

Given our society's increased exposure to multimedia formats on social media platforms, efforts to understand how digital content impacts people's emotions are burgeoning. As such, we introduce a U.S. gun violence news dataset that contains news headline and image pairings from 840 news articles with 15K high-quality, crowdsourced annotations on emotional responses to the news pairings. We created three experimental conditions for the annotation process: two with a single modality (headline or image only), and one multimodal (headline and image together). In contrast to prior works on affectively-annotated data, our dataset includes annotations on the dominant emotion experienced with the content, the intensity of the selected emotion and an open-ended, written component. By collecting annotations on different modalities of the same news content pairings, we explore the relationship between image and text influence on human emotional response. We offer initial analysis on our dataset, showing the nuanced affective differences that appear due to modality and individual factors such as political leaning and media consumption habits. Our dataset is made publicly available to facilitate future research in affective computing.

Original language	English
Title of host publication	Language Resources and Evaluation Conference, LREC 2022
Editors	Nicoletta Calzolari, Frederic Bechet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Helene Mazo, Jan Odijk, Stelios Piperidis
Place of Publication	Paris France
Publisher	European Language Resources Association (ELRA)
Pages	2507-2516
Number of pages	10
ISBN (Electronic)	9791095546726
Publication status	Published - 2022
Externally published	Yes
Event	International Conference on Language Resources and Evaluation Conference 2022 - Marseille, France Duration: 20 Jun 2022 → 25 Jun 2022 Conference number: 13th https://aclanthology.org/volumes/2022.lrec-1/ (Proceedings)

Conference

Conference	International Conference on Language Resources and Evaluation Conference 2022
Abbreviated title	LREC 2022
Country/Territory	France
City	Marseille
Period	20/06/22 → 25/06/22
Internet address	https://aclanthology.org/volumes/2022.lrec-1/ (Proceedings)

Keywords

affective computing
multimodal news data
crowdsourcing
language resources

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

420218655-oaFinal published version, 3.49 MB

https://aclanthology.org/2022.lrec-1.267

Cite this

Reardon, C., Paik, S., Gao, G., Parekh, M., Zhao, Y., Guo, L., Betke, M., & Wijaya, D. (2022). BU-NEmo: an affective dataset of gun violence news. In N. Calzolari, F. Bechet, P. Blache, K. Choukri, C. Cieri, T. Declerck, S. Goggi, H. Isahara, B. Maegaard, J. Mariani, H. Mazo, J. Odijk, & S. Piperidis (Eds.), Language Resources and Evaluation Conference, LREC 2022 (pp. 2507-2516). European Language Resources Association (ELRA). https://aclanthology.org/2022.lrec-1.267

Reardon, Carley ; Paik, Sejin ; Gao, Ge et al. / BU-NEmo : an affective dataset of gun violence news. Language Resources and Evaluation Conference, LREC 2022. editor / Nicoletta Calzolari ; Frederic Bechet ; Philippe Blache ; Khalid Choukri ; Christopher Cieri ; Thierry Declerck ; Sara Goggi ; Hitoshi Isahara ; Bente Maegaard ; Joseph Mariani ; Helene Mazo ; Jan Odijk ; Stelios Piperidis. Paris France : European Language Resources Association (ELRA), 2022. pp. 2507-2516

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title = "BU-NEmo: an affective dataset of gun violence news",

abstract = "Given our society's increased exposure to multimedia formats on social media platforms, efforts to understand how digital content impacts people's emotions are burgeoning. As such, we introduce a U.S. gun violence news dataset that contains news headline and image pairings from 840 news articles with 15K high-quality, crowdsourced annotations on emotional responses to the news pairings. We created three experimental conditions for the annotation process: two with a single modality (headline or image only), and one multimodal (headline and image together). In contrast to prior works on affectively-annotated data, our dataset includes annotations on the dominant emotion experienced with the content, the intensity of the selected emotion and an open-ended, written component. By collecting annotations on different modalities of the same news content pairings, we explore the relationship between image and text influence on human emotional response. We offer initial analysis on our dataset, showing the nuanced affective differences that appear due to modality and individual factors such as political leaning and media consumption habits. Our dataset is made publicly available to facilitate future research in affective computing.",

keywords = "affective computing, multimodal news data, crowdsourcing, language resources",

author = "Carley Reardon and Sejin Paik and Ge Gao and Meet Parekh and Yanling Zhao and Lei Guo and Margrit Betke and Derry Wijaya",

note = "Funding Information: This work is supported in part by the U.S. National Science Foundation (NSF), grant 1838193. Any opinions, findings, and conclusions or recommendations expressed are those of the authors and do not necessarily reflect the views of NSF. We would also like to thank Yang (Alex) Yu, Prakash Ishwar, and Wenda Qin for their early input and efforts on this project. Publisher Copyright: {\textcopyright} European Language Resources Association (ELRA), licensed under CC-BY-NC-4.0.; International Conference on Language Resources and Evaluation Conference 2022, LREC 2022 ; Conference date: 20-06-2022 Through 25-06-2022",

year = "2022",

language = "English",

pages = "2507--2516",

editor = "Nicoletta Calzolari and Frederic Bechet and Philippe Blache and Khalid Choukri and Christopher Cieri and Thierry Declerck and Sara Goggi and Hitoshi Isahara and Bente Maegaard and Joseph Mariani and Helene Mazo and Jan Odijk and Stelios Piperidis",

booktitle = "Language Resources and Evaluation Conference, LREC 2022",

publisher = "European Language Resources Association (ELRA)",

url = "https://aclanthology.org/volumes/2022.lrec-1/",

}

Reardon, C, Paik, S, Gao, G, Parekh, M, Zhao, Y, Guo, L, Betke, M & Wijaya, D 2022, BU-NEmo: an affective dataset of gun violence news. in N Calzolari, F Bechet, P Blache, K Choukri, C Cieri, T Declerck, S Goggi, H Isahara, B Maegaard, J Mariani, H Mazo, J Odijk & S Piperidis (eds), Language Resources and Evaluation Conference, LREC 2022. European Language Resources Association (ELRA), Paris France, pp. 2507-2516, International Conference on Language Resources and Evaluation Conference 2022, Marseille, France, 20/06/22. <https://aclanthology.org/2022.lrec-1.267>

BU-NEmo: an affective dataset of gun violence news. / Reardon, Carley; Paik, Sejin; Gao, Ge et al.
Language Resources and Evaluation Conference, LREC 2022. ed. / Nicoletta Calzolari; Frederic Bechet; Philippe Blache; Khalid Choukri; Christopher Cieri; Thierry Declerck; Sara Goggi; Hitoshi Isahara; Bente Maegaard; Joseph Mariani; Helene Mazo; Jan Odijk; Stelios Piperidis. Paris France: European Language Resources Association (ELRA), 2022. p. 2507-2516.

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

TY - GEN

T1 - BU-NEmo

T2 - International Conference on Language Resources and Evaluation Conference 2022

AU - Reardon, Carley

AU - Paik, Sejin

AU - Gao, Ge

AU - Parekh, Meet

AU - Zhao, Yanling

AU - Guo, Lei

AU - Betke, Margrit

AU - Wijaya, Derry

N1 - Conference code: 13th

PY - 2022

Y1 - 2022

N2 - Given our society's increased exposure to multimedia formats on social media platforms, efforts to understand how digital content impacts people's emotions are burgeoning. As such, we introduce a U.S. gun violence news dataset that contains news headline and image pairings from 840 news articles with 15K high-quality, crowdsourced annotations on emotional responses to the news pairings. We created three experimental conditions for the annotation process: two with a single modality (headline or image only), and one multimodal (headline and image together). In contrast to prior works on affectively-annotated data, our dataset includes annotations on the dominant emotion experienced with the content, the intensity of the selected emotion and an open-ended, written component. By collecting annotations on different modalities of the same news content pairings, we explore the relationship between image and text influence on human emotional response. We offer initial analysis on our dataset, showing the nuanced affective differences that appear due to modality and individual factors such as political leaning and media consumption habits. Our dataset is made publicly available to facilitate future research in affective computing.

AB - Given our society's increased exposure to multimedia formats on social media platforms, efforts to understand how digital content impacts people's emotions are burgeoning. As such, we introduce a U.S. gun violence news dataset that contains news headline and image pairings from 840 news articles with 15K high-quality, crowdsourced annotations on emotional responses to the news pairings. We created three experimental conditions for the annotation process: two with a single modality (headline or image only), and one multimodal (headline and image together). In contrast to prior works on affectively-annotated data, our dataset includes annotations on the dominant emotion experienced with the content, the intensity of the selected emotion and an open-ended, written component. By collecting annotations on different modalities of the same news content pairings, we explore the relationship between image and text influence on human emotional response. We offer initial analysis on our dataset, showing the nuanced affective differences that appear due to modality and individual factors such as political leaning and media consumption habits. Our dataset is made publicly available to facilitate future research in affective computing.

KW - affective computing

KW - multimodal news data

KW - crowdsourcing

KW - language resources

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M3 - Conference Paper

SP - 2507

EP - 2516

BT - Language Resources and Evaluation Conference, LREC 2022

A2 - Calzolari, Nicoletta

A2 - Bechet, Frederic

A2 - Blache, Philippe

A2 - Choukri, Khalid

A2 - Cieri, Christopher

A2 - Declerck, Thierry

A2 - Goggi, Sara

A2 - Isahara, Hitoshi

A2 - Maegaard, Bente

A2 - Mariani, Joseph

A2 - Mazo, Helene

A2 - Odijk, Jan

A2 - Piperidis, Stelios

PB - European Language Resources Association (ELRA)

CY - Paris France

Y2 - 20 June 2022 through 25 June 2022

ER -

Reardon C, Paik S, Gao G, Parekh M, Zhao Y, Guo L et al. BU-NEmo: an affective dataset of gun violence news. In Calzolari N, Bechet F, Blache P, Choukri K, Cieri C, Declerck T, Goggi S, Isahara H, Maegaard B, Mariani J, Mazo H, Odijk J, Piperidis S, editors, Language Resources and Evaluation Conference, LREC 2022. Paris France: European Language Resources Association (ELRA). 2022. p. 2507-2516

BU-NEmo: an affective dataset of gun violence news

Abstract

Conference

Keywords

UN SDGs

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