Facebook and Public Health

A Study to Understand Facebook Post Performance with Organizations’ Strategy

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningpeer review

Resumé

This paper reports on a survey about the perceptions and practices of social media managers and experts in the area of public health. We have collected Facebook data from 153 public health care organizations and conducted a survey on them. 12% of organizations responded to the questionnaire. The survey results were combined with the findings from our previous work of applying clustering and supervised learning algorithms on big social data from the official Facebook walls of these organizations. In earlier research, we showed that the most successful strategy that leads to higher post engagement is visual content. In this paper, we investigated if organisations pursue this strategy or some other strategy that was successful and has not been uncovered by the machine learning algorithms. Performance of each organisation on Facebook is based on the number of posts (volume share) and the number of actions (value share). Calculation of performance with number of actions in the numerator and number of posts in the denominator reduces possible bias in the conclusions due to the varied size of organizations on social media. Moreover, our survey attempts to better understand the behaviour of organizations and to explain why almost half of the public health care content posted on Facebook is in the form of a short text message, where as the information can be communicated through seven other post types. Similar patterns and characteristics for different engagement clusters, also high and low performing companies suggests that a mixed-methods research approach consisting of machine learning techniques combined with expert knowledge using qualitative methods can offer important insights.
OriginalsprogEngelsk
TitelProceedings. 2017 IEEE International Conference on Big Data : IEEE Big Data 2017
RedaktørerJian-Yun Nie, Zoran Obradovic, Toyotaro Suzumura, Rumi Ghosh, Raghunath Nambiar, Chonggang Wang, Hui Zang, Ricardo Baeza-Yates, Xiaohua Hu, Jeremy Kepner, Alfredo Cuzzocrea, Jian Tang, Masashi Toyoda
Antal sider10
Udgivelses stedLos Alamitos, CA
ForlagIEEE
Publikationsdato2017
Sider3123-3132
ISBN (Trykt)9781538627167
ISBN (Elektronisk)9781538627150, 9781538627143
DOI
StatusUdgivet - 2017
Begivenhed2017 IEEE International Conference on Big Data - Boston, USA
Varighed: 11 dec. 201714 dec. 2017
Konferencens nummer: 5
http://cci.drexel.edu/bigdata/bigdata2017/

Konference

Konference2017 IEEE International Conference on Big Data
Nummer5
LandUSA
ByBoston
Periode11/12/201714/12/2017
Internetadresse

Bibliografisk note

CBS Bibliotek har ikke adgang til materialet

Emneord

  • Big social data
  • Social media performance
  • Social media strategy
  • Facebook data

Citer dette

Straton, N., Vatrapu, R., & Mukkamala, R. R. (2017). Facebook and Public Health: A Study to Understand Facebook Post Performance with Organizations’ Strategy. I J-Y. Nie, Z. Obradovic, T. Suzumura, R. Ghosh, R. Nambiar, C. Wang, H. Zang, R. Baeza-Yates, X. Hu, J. Kepner, A. Cuzzocrea, J. Tang, ... M. Toyoda (red.), Proceedings. 2017 IEEE International Conference on Big Data: IEEE Big Data 2017 (s. 3123-3132). Los Alamitos, CA: IEEE. https://doi.org/10.1109/BigData.2017.8258288
Straton, Nadiya ; Vatrapu, Ravi ; Mukkamala, Raghava Rao. / Facebook and Public Health : A Study to Understand Facebook Post Performance with Organizations’ Strategy. Proceedings. 2017 IEEE International Conference on Big Data: IEEE Big Data 2017. red. / Jian-Yun Nie ; Zoran Obradovic ; Toyotaro Suzumura ; Rumi Ghosh ; Raghunath Nambiar ; Chonggang Wang ; Hui Zang ; Ricardo Baeza-Yates ; Xiaohua Hu ; Jeremy Kepner ; Alfredo Cuzzocrea ; Jian Tang ; Masashi Toyoda. Los Alamitos, CA : IEEE, 2017. s. 3123-3132
@inproceedings{4abf71b5fdaf4ab8903d8239d11d7f2c,
title = "Facebook and Public Health: A Study to Understand Facebook Post Performance with Organizations’ Strategy",
abstract = "This paper reports on a survey about the perceptions and practices of social media managers and experts in the area of public health. We have collected Facebook data from 153 public health care organizations and conducted a survey on them. 12{\%} of organizations responded to the questionnaire. The survey results were combined with the findings from our previous work of applying clustering and supervised learning algorithms on big social data from the official Facebook walls of these organizations. In earlier research, we showed that the most successful strategy that leads to higher post engagement is visual content. In this paper, we investigated if organisations pursue this strategy or some other strategy that was successful and has not been uncovered by the machine learning algorithms. Performance of each organisation on Facebook is based on the number of posts (volume share) and the number of actions (value share). Calculation of performance with number of actions in the numerator and number of posts in the denominator reduces possible bias in the conclusions due to the varied size of organizations on social media. Moreover, our survey attempts to better understand the behaviour of organizations and to explain why almost half of the public health care content posted on Facebook is in the form of a short text message, where as the information can be communicated through seven other post types. Similar patterns and characteristics for different engagement clusters, also high and low performing companies suggests that a mixed-methods research approach consisting of machine learning techniques combined with expert knowledge using qualitative methods can offer important insights.",
keywords = "Big social data, Social media performance, Social media strategy, Facebook data, Big social data, Social media performance, Social media strategy, Facebook data",
author = "Nadiya Straton and Ravi Vatrapu and Mukkamala, {Raghava Rao}",
note = "CBS Library does not have access to the material",
year = "2017",
doi = "10.1109/BigData.2017.8258288",
language = "English",
isbn = "9781538627167",
pages = "3123--3132",
editor = "Jian-Yun Nie and Zoran Obradovic and Toyotaro Suzumura and Rumi Ghosh and Raghunath Nambiar and Chonggang Wang and Hui Zang and Ricardo Baeza-Yates and Xiaohua Hu and Jeremy Kepner and Alfredo Cuzzocrea and Jian Tang and Masashi Toyoda",
booktitle = "Proceedings. 2017 IEEE International Conference on Big Data",
publisher = "IEEE",
address = "United States",

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Straton, N, Vatrapu, R & Mukkamala, RR 2017, Facebook and Public Health: A Study to Understand Facebook Post Performance with Organizations’ Strategy. i J-Y Nie, Z Obradovic, T Suzumura, R Ghosh, R Nambiar, C Wang, H Zang, R Baeza-Yates, X Hu, J Kepner, A Cuzzocrea, J Tang & M Toyoda (red), Proceedings. 2017 IEEE International Conference on Big Data: IEEE Big Data 2017. IEEE, Los Alamitos, CA, s. 3123-3132, 2017 IEEE International Conference on Big Data, Boston, USA, 11/12/2017. https://doi.org/10.1109/BigData.2017.8258288

Facebook and Public Health : A Study to Understand Facebook Post Performance with Organizations’ Strategy. / Straton, Nadiya; Vatrapu, Ravi; Mukkamala, Raghava Rao.

Proceedings. 2017 IEEE International Conference on Big Data: IEEE Big Data 2017. red. / Jian-Yun Nie; Zoran Obradovic; Toyotaro Suzumura; Rumi Ghosh; Raghunath Nambiar; Chonggang Wang; Hui Zang; Ricardo Baeza-Yates; Xiaohua Hu; Jeremy Kepner; Alfredo Cuzzocrea; Jian Tang; Masashi Toyoda. Los Alamitos, CA : IEEE, 2017. s. 3123-3132.

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningpeer review

TY - GEN

T1 - Facebook and Public Health

T2 - A Study to Understand Facebook Post Performance with Organizations’ Strategy

AU - Straton, Nadiya

AU - Vatrapu, Ravi

AU - Mukkamala, Raghava Rao

N1 - CBS Library does not have access to the material

PY - 2017

Y1 - 2017

N2 - This paper reports on a survey about the perceptions and practices of social media managers and experts in the area of public health. We have collected Facebook data from 153 public health care organizations and conducted a survey on them. 12% of organizations responded to the questionnaire. The survey results were combined with the findings from our previous work of applying clustering and supervised learning algorithms on big social data from the official Facebook walls of these organizations. In earlier research, we showed that the most successful strategy that leads to higher post engagement is visual content. In this paper, we investigated if organisations pursue this strategy or some other strategy that was successful and has not been uncovered by the machine learning algorithms. Performance of each organisation on Facebook is based on the number of posts (volume share) and the number of actions (value share). Calculation of performance with number of actions in the numerator and number of posts in the denominator reduces possible bias in the conclusions due to the varied size of organizations on social media. Moreover, our survey attempts to better understand the behaviour of organizations and to explain why almost half of the public health care content posted on Facebook is in the form of a short text message, where as the information can be communicated through seven other post types. Similar patterns and characteristics for different engagement clusters, also high and low performing companies suggests that a mixed-methods research approach consisting of machine learning techniques combined with expert knowledge using qualitative methods can offer important insights.

AB - This paper reports on a survey about the perceptions and practices of social media managers and experts in the area of public health. We have collected Facebook data from 153 public health care organizations and conducted a survey on them. 12% of organizations responded to the questionnaire. The survey results were combined with the findings from our previous work of applying clustering and supervised learning algorithms on big social data from the official Facebook walls of these organizations. In earlier research, we showed that the most successful strategy that leads to higher post engagement is visual content. In this paper, we investigated if organisations pursue this strategy or some other strategy that was successful and has not been uncovered by the machine learning algorithms. Performance of each organisation on Facebook is based on the number of posts (volume share) and the number of actions (value share). Calculation of performance with number of actions in the numerator and number of posts in the denominator reduces possible bias in the conclusions due to the varied size of organizations on social media. Moreover, our survey attempts to better understand the behaviour of organizations and to explain why almost half of the public health care content posted on Facebook is in the form of a short text message, where as the information can be communicated through seven other post types. Similar patterns and characteristics for different engagement clusters, also high and low performing companies suggests that a mixed-methods research approach consisting of machine learning techniques combined with expert knowledge using qualitative methods can offer important insights.

KW - Big social data

KW - Social media performance

KW - Social media strategy

KW - Facebook data

KW - Big social data

KW - Social media performance

KW - Social media strategy

KW - Facebook data

U2 - 10.1109/BigData.2017.8258288

DO - 10.1109/BigData.2017.8258288

M3 - Article in proceedings

SN - 9781538627167

SP - 3123

EP - 3132

BT - Proceedings. 2017 IEEE International Conference on Big Data

A2 - Nie, Jian-Yun

A2 - Obradovic, Zoran

A2 - Suzumura, Toyotaro

A2 - Ghosh, Rumi

A2 - Nambiar, Raghunath

A2 - Wang, Chonggang

A2 - Zang, Hui

A2 - Baeza-Yates, Ricardo

A2 - Hu, Xiaohua

A2 - Kepner, Jeremy

A2 - Cuzzocrea, Alfredo

A2 - Tang, Jian

A2 - Toyoda, Masashi

PB - IEEE

CY - Los Alamitos, CA

ER -

Straton N, Vatrapu R, Mukkamala RR. Facebook and Public Health: A Study to Understand Facebook Post Performance with Organizations’ Strategy. I Nie J-Y, Obradovic Z, Suzumura T, Ghosh R, Nambiar R, Wang C, Zang H, Baeza-Yates R, Hu X, Kepner J, Cuzzocrea A, Tang J, Toyoda M, red., Proceedings. 2017 IEEE International Conference on Big Data: IEEE Big Data 2017. Los Alamitos, CA: IEEE. 2017. s. 3123-3132 https://doi.org/10.1109/BigData.2017.8258288