640
Views
1
CrossRef citations to date
0
Altmetric
Research Article

Collective Information Seeking During a Health Crisis : Predictors of Google Trends During COVID-19

ORCID Icon &

References

  • Adebayo, G., Neumark, Y., Gesser-Edelsburg, A., Ahmad, W. A., & Levine, H. (2017). Zika pandemic online trends, incidence and health risk communication: A time trend study. BMJ Global Health, 2(3), e000296. https://doi.org/10.1136/bmjgh-2017-000296
  • Allcott, H., Boxell, L., Conway, J., Gentzkow, M., Thaler, M., & Yang, D. (2020). Polarization and public health: Partisan differences in social distancing during the coronavirus pandemic. Journal of Public Economics, 191, 104254. https://doi.org/10.1016/j.jpubeco.2020.104254
  • Ansolabehere, S., & Iyengar, S. (1994). Riding the wave and claiming ownership over issues: The joint effects of advertising and news coverage in campaigns. Public Opinion Quarterly, 58(3), 335–357. https://doi.org/10.1086/269431
  • Avery, E. J., & Park, S. (2021). Perceived knowledge as [protective] power: Parents’ protective efficacy, information-seeking, and scrutiny during COVID-19. Health Communication, 36(1), 81–88. https://doi.org/10.1080/10410236.2020.1847438
  • Ball-Rokeach, S. J. (1985). The origins of individual media-system dependency: A sociological framework. Communication Research, 12(4), 485–510. https://doi.org/10.1177/009365085012004003
  • Ball-Rokeach, S. J., Kim, Y.-C., & Matei, S. (2001). Storytelling neighborhood: Paths to belonging in diverse urban environments. Communication Research, 28(4), 392–428. https://doi.org/10.1177/009365001028004003
  • Bond, R. M. (2018). Contagion in social attitudes about prejudice. Social Influence, 13(2), 104–116. https://doi.org/10.1080/15534510.2018.1453374
  • Bond, R. M., & Bushman, B. J. (2017). The contagious spread of violence among US adolescents through social networks. American Journal of Public Health, 107(2), 288–294. https://doi.org/10.2105/AJPH.2016.303550
  • Bond, R. M., Chykina, V., & Jones, J. J. (2017). Social network effects on academic achievement. The Social Science Journal, 54(4), 438–449. https://doi.org/10.1016/j.soscij.2017.06.001
  • Bond, R. M., Fariss, C. J., Jones, J. J., Kramer, A. D. I., Marlow, C., Settle, J. E., & Fowler, J. H. (2012). A 61-million-person experiment in social influence and political mobilization. Nature, 489(7415), 295–298, Article 7415. https://doi.org/10.1038/nature11421
  • Britt, R. K., Britt, B. C., Panek, E., & Lee, J. (2021). Communication expressed on the COVID-19 subreddit in the midst of a global pandemic. Health Communication, 1–11. https://doi.org/10.1080/10410236.2021.1994190
  • Burns, W. J., & Slovic, P. (2012). Risk perception and behaviors: Anticipating and responding to crises. Risk Analysis, 32(4), 579–582. https://doi.org/10.1111/j.1539-6924.2012.01791.x
  • Chaffee, S. H., & Roser, C. (1986). Involvement and the consistency of knowledge, attitudes, and behaviors. Communication Research, 13(3), 373–399. https://doi.org/10.1177/009365086013003006
  • Charoenwong, B., Kwan, A., & Pursiainen, V. (2020). Social connections with COVID-19–affected areas increase compliance with mobility restrictions. Science Advances, 6(47), eabc3054. https://doi.org/10.1126/sciadv.abc3054
  • Christakis, N. A., & Fowler, J. H. (2013). Social contagion theory: Examining dynamic social networks and human behavior. Statistics in Medicine, 32(4), 556–577. https://doi.org/10.1002/sim.5408
  • Czaja, R., Manfredi, C., & Price, J. (2003). The determinants and consequences of information seeking among cancer patients. Journal of Health Communication, 8(6), 529–562. https://doi.org/10.1080/716100418
  • Dailey, D., & Starbird, K. (2015). “It’s raining dispersants”: Collective sensemaking of complex information in crisis contexts. In Proceedings of the 18th ACM Conference Companion on Computer Supported Cooperative Work & Social Computing (pp. 155–158). https://doi.org/10.1145/2685553.2698995
  • Dutta-Bergman, M. J. (2004). Complementarity in consumption of news types across traditional and new media. Journal of Broadcasting & Electronic Media, 48(1), 41–60. https://doi.org/10.1207/s15506878jobem4801_3
  • Ettema, J. S., Brown, J. W., & Luepker, R. V. (1983). Knowledge gap effects in a health information campaign. Public Opinion Quarterly, 47(4), 516–527. https://doi.org/10.1086/268809
  • Evanega, S., Lynas, M., Adams, J., & Smolenyak, K. (2020). Coronavirus misinformation: Quantifying sources and themes in the COVID-19 ‘infodemic.’ JMIR Preprints, 19(10). https://allianceforscience.org/wp-content/uploads/2020/10/Evanega-et-al-Coronavirus-misinformation-submitted_07_23_20-1.pdf
  • Eysenbach, G. (2009). Infodemiology and infoveillance: Framework for an emerging set of public health informatics methods to analyze search, communication and publication behavior on the Internet. Journal of Medical Internet Research, 11(1), e1157. https://doi.org/10.2196/jmir.1157
  • Fowler, J. H., & Christakis, N. A. (2008). Dynamic spread of happiness in a large social network: Longitudinal analysis over 20 years in the Framingham Heart Study. The BMJ, 337(dec04 2), a2338. https://doi.org/10.1136/bmj.a2338
  • Gadarian, S. K., Goodman, S. W., & Pepinsky, T. B. (2021). Partisanship, health behavior, and policy attitudes in the early stages of the COVID-19 pandemic. Plos One, 16(4), e0249596. https://doi.org/10.1371/journal.pone.0249596
  • Gollwitzer, A., McLoughlin, K., Martel, C., Marshall, J., Höhs, J. M., & Bargh, J. A. (2021). Linking self-reported social distancing to real-world behavior during the COVID-19 pandemic. Social Psychological and Personality Science, 19485506211018132. https://doi.org/10.1177/19485506211018132
  • Gottfried, J. (2021). Republicans less likely to trust their main news source if they see it as ‘mainstream’; Democrats more likely. Pew Research Center. https://www.pewresearch.org/fact-tank/2021/07/01/republicans-less-likely-to-trust-their-main-news-source-if-they-see-it-as-mainstream-democrats-more-likely/
  • Griffin, R. J., Dunwoody, S., & Neuwirth, K. (1999). Proposed model of the relationship of risk information seeking and processing to the development of preventive behaviors. Environmental Research, 80(2), S230–245. https://doi.org/10.1006/enrs.1998.3940
  • Griffin, R. J., Neuwirth, K., Dunwoody, S., & Giese, J. (2004). Information sufficiency and risk communication. Media Psychology, 6(1), 23–61. https://doi.org/10.1207/s1532785xmep0601_2
  • Grossman, G., Kim, S., Rexer, J. M., & Thirumurthy, H. (2020). Political partisanship influences behavioral responses to governors’ recommendations for COVID-19 prevention in the United States. Proceedings of the National Academy of Sciences, 117(39), 24144–24153. https://doi.org/10.1073/pnas.2007835117
  • Hart, P. S., Chinn, S., & Soroka, S. (2020). Politicization and polarization in COVID-19 news coverage. Science Communication, 42(5), 679–697. https://doi.org/10.1177/1075547020950735
  • Henrich, N., & Holmes, B. (2011). Communicating during a pandemic: Information the public wants about the disease and new vaccines and drugs. Health Promotion Practice, 12(4), 610–619. https://doi.org/10.1177/1524839910363536
  • Hovick, S., Freimuth, V. S., Johnson-Turbes, A., & Chervin, D. D. (2011). Multiple health risk perception and information processing among African Americans and Whites living in poverty. Risk Analysis, 31(11), 1789–1799. https://doi.org/10.1111/j.1539-6924.2011.01621.x
  • Huang, X., Baade, P., Youlden, D. R., Youl, P. H., Hu, W., & Kimlin, M. G. (2017). Google as a cancer control tool in Queensland. BMC Cancer, 17(1), 816. https://doi.org/10.1186/s12885-017-3828-x
  • Huang, Y., Shen, C., & Contractor, N. S. (2013). Distance matters: Exploring proximity and homophily in virtual world networks. Decision Support Systems, 55(4), 969–977. https://doi.org/10.1016/j.dss.2013.01.006
  • Huang, Y., & Yang, C. (2020). A metacognitive approach to reconsidering risk perceptions and uncertainty: Understand information seeking during COVID-19. Science Communication, 42(5), 616–642. https://doi.org/10.1177/1075547020959818
  • Janz, N. K., & Becker, M. H. (1984). The health belief model: A decade later. Health Education Quarterly, 11(1), 1–47. https://doi.org/10.1177/109019818401100101
  • Johnson, J. D., & Meischke, H. (1993). A comprehensive model of cancer-related information seeking applied to magazines. Human Communication Research, 19(3), 343–367. https://doi.org/10.1111/j.1468-2958.1993.tb00305.x
  • Ju, I., Ohs, J., Park, T., & Hinsley, A. (2021). Interpersonal communication influence on health-protective behaviors amid the COVID-19 crisis. Health Communication, 1–12. https://doi.org/10.1080/10410236.2021.1956038
  • Kahan, D. M., Braman, D., Monahan, J., Callahan, L., & Peters, E. (2010). Cultural cognition and public policy: The case of outpatient commitment laws. Law and Human Behavior, 34(2), 118–140. https://doi.org/10.1007/s10979-008-9174-4
  • Kahlor, L. (2010). PRISM: A planned risk information seeking model. Health Communication, 25(4), 345–356. https://doi.org/10.1080/10410231003775172
  • Kasperson, R. E., Renn, O., Slovic, P., Brown, H. S., Emel, J., Goble, R., Kasperson, J. X., & Ratick, S. (1988). The social amplification of risk: A conceptual framework. Risk Analysis, 8(2), 177–187. https://doi.org/10.1111/j.1539-6924.1988.tb01168.x
  • Kasperson, R. E., Webler, T., Ram, B., & Sutton, J. (2022). The social amplification of risk framework: New perspectives. Risk Analysis, 42(7), 1367–1380. https://doi.org/10.1111/risa.13926
  • Katz, E. (1957). The two-step flow of communication: An up-to-date report on an hypothesis. Public Opinion Quarterly, 21(1), 61–78. https://doi.org/10.1086/266687
  • Kristensen, K., Lorenz, E., May, J., & Strauss, R. (2021). Exploring the use of web searches for risk communication during COVID-19 in Germany. Scientific Reports, 11(1), Article 1. https://doi.org/10.1038/s41598-021-85873-4
  • Lazer, D. M., Kennedy, R., King, G., & Vespignani, A. (2014). The parable of Google Flu: Traps in big data analysis. http://dash.harvard.edu/handle/1/12016836
  • Lee, E. W. J., Bekalu, M. A., McCloud, R. F., & Viswanath, K. (2021). Toward an extended infodemiology framework: Leveraging social media data and web search queries as digital pulse on cancer communication. Health Communication, 38(2), 1–14. https://doi.org/10.1080/10410236.2021.1951957
  • Lin, Y.-H., Liu, C.-H., & Chiu, Y.-C. (2020). Google searches for the keywords of “wash hands” predict the speed of national spread of COVID-19 outbreak among 21 countries. Brain, Behavior, and Immunity, 87, 30–32. https://doi.org/10.1016/j.bbi.2020.04.020
  • Lin, T., & Nan, X. (2022). A scoping review of emerging COVID-19 health communication research in communication and media journals. Health Communication, 1–12. https://doi.org/10.1080/10410236.2022.2091916
  • Liu, Z., & Yang, Z. J. (2021). Public support for COVID-19 responses: Cultural cognition, risk perception, and emotions. Health Communication, 1–11. https://doi.org/10.1080/10410236.2021.1965710
  • Maddux, J. E., & Rogers, R. W. (1983). Protection motivation and self-efficacy: A revised theory of fear appeals and attitude change. Journal of Experimental Social Psychology, 19(5), 469–479. https://doi.org/10.1016/0022-1031(83)90023-9
  • Mangono, T., Smittenaar, P., Caplan, Y., Huang, V. S., Sutermaster, S., Kemp, H., & Sgaier, S. K. (2021). Information-seeking patterns during the COVID-19 pandemic across the United States: Longitudinal analysis of google trends data. Journal of Medical Internet Research, 23(5), e22933. https://doi.org/10.2196/22933
  • Margolin, D. B. (2019). Computational contributions: A symbiotic approach to integrating big, observational data studies into the communication field. Communication Methods and Measures, 13(4), 229–247. https://doi.org/10.1080/19312458.2019.1639144
  • Ming, W., Huang, F., Chen, Q., Liang, B., Jiao, A., Liu, T., Wu, H., Akinwunmi, B., Li, J., Liu, G., Zhang, C. J. P., Huang, J., & Liu, Q. (2021). Understanding health communication through Google trends and news coverage for COVID-19: Multinational study in eight countries. JMIR Public Health and Surveillance, 7(12), e26644. https://doi.org/10.2196/26644
  • Munger, K., & Phillips, J. (2020). Right-Wing YouTube: A supply and demand perspective. The International Journal of Press/Politics, 27(1), 194016122096476. https://doi.org/10.1177/1940161220964767
  • Newman, T. P., Nisbet, E. C., & Nisbet, M. C. (2018). Climate change, cultural cognition, and media effects: Worldviews drive news selectivity, biased processing, and polarized attitudes. Public Understanding of Science, 27(8), 985–1002. https://doi.org/10.1177/0963662518801170
  • Niederdeppe, J., Hornik, R. C., Kelly, B. J., Frosch, D. L., Romantan, A., Stevens, R. S., Barg, F. K., Weiner, J. L., & Schwartz, J. S. (2007). Examining the dimensions of cancer-related information seeking and scanning behavior. Health Communication, 22(2), 153–167. https://doi.org/10.1080/10410230701454189
  • O’Shea, B. A., & Ueda, M. (2021). Who is more likely to ignore experts’ advice related to COVID-19? Preventive Medicine Reports, 23, 101470. https://doi.org/10.1016/j.pmedr.2021.101470
  • Ou, M., & Ho, S. S. (2022). A meta-analysis of factors related to health information seeking: An integration from six theoretical frameworks. Communication Research, 49(4), 567–593. https://doi.org/10.1177/00936502211043024
  • Painter, M., & Qiu, T. (2021). Political beliefs affect compliance with government mandates. Journal of Economic Behavior & Organization, 185, 688–701. https://doi.org/10.1016/j.jebo.2021.03.019
  • Pan, P.-L., & Meng, J. (2016). Media frames across stages of health crisis: A crisis management approach to news coverage of flu pandemic. Journal of Contingencies and Crisis Management, 24(2), 95–106. https://doi.org/10.1111/1468-5973.12105
  • Pew Research Center. (2020). Americans see multiple threats from the Coronavirus – And concerns are growing. https://www.pewresearch.org/politics/2020/03/18/u-s-public-sees-multiple-threats-from-the-coronavirus-and-concerns-are-growing/
  • Pollock, J. C. (2015). Community structure model. In E. Donsbach (Eds.), The international encyclopedia of communication (pp. 1–5). John Wiley & Sons, Ltd. https://doi.org/10.1002/9781405186407.wbiecc109.pub3
  • Ragas, M. W., Tran, H. L., & Martin, J. A. (2014). Media-induced or search-driven? Journalism Studies, 15(1), 48–63. https://doi.org/10.1080/1461670X.2013.793509
  • Rainie, H., & Wellman, B. (2012). Networked: The new social operating system. Mit Press.
  • Rimal, R. N., Flora, J. A., & Schooler, C. (1999). Achieving improvements in overall health orientation: Effects of campaign exposure, information seeking, and health media use. Communication Research, 26(3), 322–348. https://doi.org/10.1177/009365099026003003
  • Rimal, R. N., & Real, K. (2003). Perceived risk and efficacy beliefs as motivators of change: Use of the Risk Perception Attitude (RPA) framework to understand health behaviors. Human Communication Research, 29(3), 370–399. https://doi.org/10.1093/hcr/29.3.370
  • Rutten, L. J. F., Squiers, L., & Hesse, B. (2006). Cancer-related information seeking: Hints from the 2003 Health Information National Trends Survey (HINTS). Journal of Health Communication, 11(sup001), 147–156. https://doi.org/10.1080/10810730600637574
  • Shteynberg, G. (2015). Shared attention. Perspectives on Psychological Science, 10(5), 579–590. https://doi.org/10.1177/1745691615589104
  • Siegrist, M., & Cvetkovich, G. (2000). Perception of hazards: The role of social trust and knowledge. Risk Analysis, 20(5), 713–720. https://doi.org/10.1111/0272-4332.205064
  • Siegrist, M., Gutscher, H., & Earle, T. C. (2005). Perception of risk: The influence of general trust, and general confidence. Journal of Risk Research, 8(2), 145–156. https://doi.org/10.1080/1366987032000105315
  • Slater, M. D., & Gleason, L. S. (2012). Contributing to theory and knowledge in quantitative communication science. Communication Methods and Measures, 6(4), 215–236. https://doi.org/10.1080/19312458.2012.732626
  • Slothuus, R. (2010). When can political parties lead public opinion? Evidence from a natural experiment. Political Communication, 27(2), 158–177. https://doi.org/10.1080/10584601003709381
  • So, J., Kuang, K., & Cho, H. (2019). Information seeking upon exposure to risk messages: Predictors, outcomes, and mediating roles of health information seeking. Communication Research, 46(5), 663–687. https://doi.org/10.1177/0093650216679536
  • Southwell, B. G., Dolina, S., Jimenez-Magdaleno, K., Squiers, L. B., & Kelly, B. J. (2016). Zika virus–related news coverage and online behavior, United States, Guatemala, and Brazil. Emerging Infectious Diseases, 22(7), 1320–1321. https://doi.org/10.3201/eid2207.160415
  • Terpstra, T. (2011). Emotions, trust, and perceived risk: Affective and cognitive routes to flood preparedness behavior. Risk Analysis, 31(10), 1658–1675. https://doi.org/10.1111/j.1539-6924.2011.01616.x
  • Vasconcellos-Silva, P. R., Carvalho, D. B. F., Trajano, V., Rocque, L. R. D. L., Sawada, A. C. M. B., & Juvanhol, L. L. (2017). Using Google trends data to study public interest in breast cancer screening in Brazil: Why not a pink February? JMIR Public Health and Surveillance, 3(2), e7015. https://doi.org/10.2196/publichealth.7015
  • Wagner, A., & Reifegerste, D. (2021). “The part played by people” in times of COVID-19: Interpersonal communication about media coverage in a pandemic crisis. Health Communication, 1–8. https://doi.org/10.1080/10410236.2021.1989786
  • Wang, X., Shi, J., & Kong, H. (2021). Online health information seeking: A review and meta-analysis. Health Communication, 36(10), 1163–1175. https://doi.org/10.1080/10410236.2020.1748829
  • Weeks, B. E., Friedenberg, L. M., Southwell, B. G., & Slater, J. S. (2012). Behavioral consequences of conflict-oriented health news coverage: The 2009 mammography guideline controversy and online information seeking. Health Communication, 27(2), 158–166. https://doi.org/10.1080/10410236.2011.571757
  • Weick, K. E. (1988). Enacted sensemaking in crisis situations. Journal of Management Studies, 25(4), 305–317. https://doi.org/10.1111/j.1467-6486.1988.tb00039.x
  • Weick, K. E., Sutcliffe, K. M., & Obstfeld, D. (2005). Organizing and the process of sensemaking. Organization Science, 16(4), 409–421. https://doi.org/10.1287/orsc.1050.0133
  • Wirz, C. D., Shao, A., Bao, L., Howell, E. L., Monroe, H., & Chen, K. (2021). Media systems and attention cycles: Volume and topics of news coverage on COVID-19 in the United States and China. Journalism & Mass Communication Quarterly, 99(4), 10776990211049456. https://doi.org/10.1177/10776990211049455
  • Witte, K. (1992). Putting the fear back into fear appeals: The extended parallel process model. Communication Monographs, 59(4), 329–349. https://doi.org/10.1080/03637759209376276
  • Yang, Q., & Cao, W. (2022). Health disparities in online COVID-19 information seeking and protective behaviors: A two-wave longitudinal study. Health Communication, 37(12), 1534–1543. https://doi.org/10.1080/10410236.2022.2056980
  • Yang, Z. J., McComas, K., Gay, G., Leonard, J. P., Dannenberg, A. J., & Dillon, H. (2010). Motivation for health information seeking and processing about clinical trial enrollment. Health Communication, 25(5), 423–436. https://doi.org/10.1080/10410236.2010.483338

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.