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Cervical cancer is the second most common cancer among women under 45 years of age. To deal with the decrease of smear test coverage in the United Kingdom, a Twitter campaign called #SmearForSmear has been launched in 2015 for the European Cervical Cancer Prevention Week. Its aim was to encourage women to take a selfie showing their lipstick going over the edge and post it on Twitter with a raising awareness message promoting cervical cancer screening. The estimated audience was 500 million people. Other public health campaigns have been launched on social media such as Movember to encourage participation and self-engagement. Their result was unsatisfactory as their aim had been diluted to become mainly a social buzz.
The objectives of this study were to identify the tweets delivering a raising awareness message promoting cervical cancer screening (sensitizing tweets) and to understand the characteristics of Twitter users posting about this campaign.
We conducted a 3-step content analysis of the English tweets tagged #SmearForSmear posted on Twitter for the 2015 European Cervical Cancer Prevention Week. Data were collected using the Twitter application programming interface. Their extraction was based on an analysis grid generated by 2 independent researchers using a thematic analysis, validated by a strong Cohen kappa coefficient. A total of 7 themes were coded for sensitizing tweets and 14 for Twitter users’ status. Verbatims were thematically and then statistically analyzed.
A total of 3019 tweets were collected and 1881 were analyzed. Moreover, 69.96% of tweets had been posted by people living in the United Kingdom. A total of 57.36% of users were women, and sex was unknown in 35.99% of cases. In addition, 54.44% of the users had posted at least one selfie with smeared lipstick. Furthermore, 32.32% of tweets were sensitizing. Independent factors associated with posting sensitizing tweets were women who experienced an abnormal smear test (OR [odds ratio] 13.456, 95% CI 3.101-58.378,
This study demonstrates that the success of a public health campaign using a social media platform depends on its ability to get its targets involved. It also suggests the need to use social marketing to help its dissemination. The clinical impact of this Twitter campaign to increase cervical cancer screening is yet to be evaluated.
Cervical cancer is the second most common cancer among women under 45 years of age and leads to significant mortality [
Social media would have a great potential to improve behavior change as interactive tools, encouraging participation and self-engagement instead of a descending information [
Public health campaigns have already tried to take advantage of the ability of social media to make a campaign viral. The amyotrophic lateral sclerosis (ALS) Ice Bucket Challenge’s goal was to mediatize and raise funds for the ALS association. The campaign had involved many celebrities worldwide. On September 1, 2014, more than 17 million videos had been shared on Facebook and had been watched more than 10 billion times by more than 440 million people [
To deal with the decrease of smear coverage in the United Kingdom, a Twitter campaign called #SmearForSmear has been launched in 2015 by Jo’s Cervical Cancer Trust for the European Cervical Cancer Prevention Week. Its goal was to encourage women to take a picture of themselves (selfie) showing their lipstick going over the edge and post it on Twitter with an awareness message promoting cervical cancer screening. The estimated audience was 500 million people [
The objectives of this study were to identify the tweets delivering raising awareness messages about cervical cancer screening and to understand the characteristics of Twitter users posting about this campaign.
We conducted a 3-step content analysis of the English tweets posted on Twitter during the 2015 European Cervical Cancer Prevention Week.
To collect the tweets, we used the Twitter application programming interface. It allows the user to conduct manual searches for keywords in tweets with specific parameters such as hashtags, language, and date range. The ones used for this research were as follows: #SmearForSmear, English language, and tweets posted between January 25, 2015 and January 31, 2015, both dates inclusive (European Cervical Cancer Prevention Week). All tweets have been manually collected and assessed. Only original tweets, rather than retweets, were analyzed. In the tweets, only the verbatims were transcribed. Hashtags and content preceded by “@” were removed if that action did not make the verbatim unintelligible. We also considered all hypertexts linked to another verbatim on another Web platform (eg, Instagram). The corresponding verbatims were transcribed only if they were informative.
A total of 3019 tweets that met the search criteria were imported into Excel for data extraction. An analysis grid had been created based on the first 200 original tweets collected and thematically analyzed by 2 independent researchers to extract the themes (topics) of tweets’ verbatims and Twitter users’ statuses. Then, this grid had been tested on 50 new tweets. No new themes had been identified, confirming that category saturation was achieved [
The following information was collected about each tweet: verbatim, posting date, retrieval date, presence of a selfie with lipstick going over the edge, picture or video referring to the campaign, user’s sex, user’s location, number of followers at the date of retrieval, and user’s status. To classify the users, we used their Twitter status. If it did not exist or was incomplete, we extracted this information from links on their Twitter profile, whenever possible. The analysis grid enlisted 14 themes regarding Twitter users’ status: health company, media company, nonhealth and nonmedia company, marketing company, fashion company, blogger or YouTuber, health professional, National Health Service (NHS), politician, woman who experienced cervical cancer or who had relatives with cervical cancer, woman with an unspecified cancer or relatives with a similar status, woman who experienced an abnormal smear test, general public, and unknown. The “unknown” status was attributed when no information to categorize the user was available. Only the “unknown,” “general public,” or “NHS” statuses were exclusive.
An initial global description of the sample has been performed, using the frequencies of the different categories for the qualitative variables. As the distribution of quantitative variables was not always Gaussian (Shapiro–Wilk test), they were expressed by their mean, standard deviation, median, minimum and maximum values, and interquartile. Comparison of means was executed through the Student test when distribution was Gaussian; otherwise, it was based on Mann–Whitney test. Comparison of qualitative variables was executed through the chi-square test for parametric tests, or Fisher exact test when the conditions for applying chi-square test were not observed. A multivariate logistic modeling process was then conducted to identify the independent factors associated with the presence of a sensitizing message in the tweets and associated with each type of sensitizing message. A “step-by-step” selection procedure of the variable was used with an input and output variable set at 0.10 and 0.05, respectively. The significance threshold was set to 5%. Statistical analysis was performed by the Department of Medical Information at Montpellier Teaching Hospital with SAS version 9.4 (SAS Institute Inc).
A total of 3019 tweets met the search criteria; 1138 tweets were removed (retweets or copies of tweets); and 1881 tweets were analyzed.
Moreover, 608 tweets (32.32%) were sensitizing. Each of them included from 1 to 5 raising awareness message. The mean number of raising awareness messages among original tweets was 0.54 (standard deviation [SD] 0.93;
Main users were people from English-speaking countries. The United Kingdom accounted for 69.96% of the posted tweets, followed by the United States (8.67%) and Australia (1.06%). Nationality was unknown in 15.20% of cases. Moreover, 57.36% of users were women, and sex was unknown in 35.99% of cases. Twitter users had a mean number of followers of 44,420.8 (SD 420,819.04). A total of 54.44% of the users had posted at least one selfie with smeared lipstick. In addition, 15.63% tweets were associated with a picture or a video referring to the #SmearForSmear campaign.
Statistically significant associations between emitting sensitizing tweets and Twitter users’ status are detailed in
The “step-by-step” selection procedure has allowed to identify independent factors influencing the sensitizing characteristic of a tweet (
Description of tweets and Twitter users.
Variable | Total, n (%) | ||
0 | 1273 (67.68) | ||
1 | 347 (18.45) | ||
2 | 149 (7.92) | ||
3 | 83 (4.41) | ||
4 | 25 (1.33) | ||
5 | 4 (0.21) | ||
608 (32.32) | |||
Incentive to carry out the smear test | 440 (23.39) | ||
Reminder of smear test preventive nature | 217 (11.54) | ||
Allusion to the mortality or morbidity of cervical cancer | 134 (7.12) | ||
Testimony of an experience related to smear test or cervical cancer | 92 (4.89) | ||
Smear test importance | 63 (3.35) | ||
Evidence of the number of cervical cancers | 41 (2.18) | ||
Low incidence of smear test | 27 (1.44) | ||
Unknown | 442 (23.5) | ||
Nonhealth and nonmedia company | 396 (21.05) | ||
Health company | 292 (15.52) | ||
Blogger or YouTuber | 262 (13.93) | ||
Media company | 240 (12.76) | ||
Fashion activity | 240 (12.76) | ||
Marketing activity | 220 (11.70) | ||
National Health Service | 79 (4.2) | ||
General public | 77 (4.09) | ||
Woman who experienced cervical cancer or who had relatives that had experienced cervical cancer | 60 (3.19) | ||
Health professional | 53 (2.82) | ||
Woman who experienced an abnormal smear test | 33 (1.75) | ||
Politician | 12 (0.64) | ||
Woman who experienced an unspecified cancer or had relatives with a similar status | 6 (0.32) |
Twitter users’ known characteristics.
Characteristics | Total, n (%) | ||
United Kingdom | 1316 (82.51) | <.001 | |
Female gender | 1079 (89.62) | <.001 | |
National Health Service | 79 (4.2) | <.001 | |
Woman who experienced an abnormal smear test | 33 (1.75) | <.001 | |
Nonhealth or nonmedia company | 396 (21.05) | <.001 | |
Media | 240 (12.76) | .045 | |
Marketing activity | 220 (11.70) | <.001 | |
Male gender | 125 (10.38) | <.001 |
Independent factors influencing the emission of sensitizing tweets.
Message of tweet, variables | Adjusted OR (95% CI) | ||
Woman who experienced an abnormal smear test | 13.456 (3.101-58.378) | <.001 | |
Female gender | 3.752 (2.133-6.598) | <.001 | |
United Kingdom | 2.097 (1.447-3.038) | <.001 | |
Nonhealth or nonmedia companya | 0.558 (0.383-0.814) | .002 | |
Female gender | 5.967 (2.606-13.659) | <.001 | |
Health company | 2.203 (1.042-4.656) | .04 | |
United Kingdom | 1.997 (1.320-3.021) | .001 | |
Selfie | 1.673 (1.228-2.280) | .001 | |
Nonhealth or nonmass media companya | 0.481 (0.310-0.746) | .001 | |
Woman who experienced an abnormal smear test | 7.365 (2.314-23.436) | <.001 | |
National Health Service | 4.266 (1.778-10.238) | .001 | |
United Kingdom | 2.888 (1.015-8.212) | .047 | |
Fashion | 2.724 (1.430-5.188) | .002 | |
Selfie | 2.158 (1.163-4.002) | .001 | |
Woman who experienced an abnormal smear test | 4.216 (1.734-10.254) | .001 | |
Politician | 3.545 (1.028-12.221) | .045 | |
Female gender | 2.555 (1.156-5.646) | .002 | |
Marketing activitya | 0.414 (0.211-0.812) | .001 | |
Woman who experienced an unspecified cancer or had relatives with a similar status | 6.359 (1.043-38.776) | <.001 | |
Woman who experienced an abnormal smear test | 5.591 (2.227-14.035) | <.001 | |
Female gender | 3.396 (1.050-10.982) | .04 | |
Woman who experienced cervical cancer or had relatives with a similar status | 2.598 (1.228-5.495) | .001 | |
United Kingdom | 2.268 (1.069-4.808) | .03 | |
Politician | 14.754 (3.074-70.816) | <.001 | |
General public | 2.913 (1.002-8.474) | .049 | |
Picture or a video linked to the #SmearForSmear campaign | 2.701 (1.372-5.318) | .004 | |
Woman who experienced an abnormal smear test | 65.364 (22.709-188.140 | <.001 | |
Woman who experienced an unspecified cancer or had relatives with a similar status | 14.371 (2.335-88.417) | .004 | |
Woman who experienced cervical cancer or had relatives with a similar status | 7.641 (3.690-15.822) | <.001 |
aStatistically significant influence on the emission of nonsensitizing tweets).
A total of 32.32% of the tweets of the #SmearforSmear campaign were sensitizing. This result was promising as it goes well beyond the results of the 2013 Movember campaign where only 0.85% of the posted tweets may raise awareness about men’s health risks [
As for the Twitter users, our expectations were broadly confirmed. From a general point of view, Twitter users posting sensitizing tweets were people personally involved in cervical cancer screening: women; women concerned by a feminine cancer, either for themselves or for their relatives; people living in the United Kingdom (where this English-speaking campaign took place); the NHS as a partner of this campaign; and women who experienced an abnormal smear test. As peers, women raised awareness by insisting on the preventive aspect of smear test and directly encouraged other women to attend their smear test. Peer influence is known as an important social lever for health-related behavior change [
Conversely, “nonsensitizing” tweets had a much greater probability to be sent either by users not directly concerned with cervical cancer such as men (exclusively feminine cancer) or by users who participated but only broadcasted information, without getting involved: media, marketing companies, and nonhealth and nonmedia companies. It questions their participation in this campaign. Was it about an opportunistic appropriation of a viral campaign? It is probably one of the main limitations of the virality of health campaigns on social media. Most tweets posted for the 2013 Movember campaign and the breast cancer prevention month did not spark conversations about prostate and testicular cancer nor promote any specific preventive behavior about breast cancer [
To our knowledge, no study analyzing the content of the #SmearForSmear campaign on Twitter has been published yet. Our findings are corroborated by the content analysis of others health campaigns on Twitter. We used a content analysis method based on a double analysis of the sensitizing capacity of each tweet, in an exploratory process. We also mined Twitter to gather information about users’ characteristics and complete the tweets’ content.
This highly demanding method made us decide early to restrict our study to one week. This choice was also relevant, in our opinion, as this campaign had been created for the European Cervical Cancer Prevention Week. Compared with other Twitter campaigns, our relatively high results must question its ability to keep a high proportion of sensitizing tweets in other countries (particularly where the cervical cancer screening is not organized) and if it remains high over time.
The choice to collect the tweets based on the hashtag #SmearForSmear may have limited their number, by omitting those not using it. As for the content analysis, 2 safeguards have been used: analyzing the content of tweets to create the categories before the study and evaluating the reproducibility of the classification by 2 independent researchers with Cohen’s kappa coefficient, which was strong in this study. The shortness of Twitter posts, limited to 140 characters, may have created a loss of information as users often used hyperlinks to be exempt from this limit. We then chose to manually mine Twitter to complete the tweets’ content and gather information about users’ characteristics.
The #SmearForSmear campaign has allowed to disseminate sensitizing messages about cervical cancer screening and to become viral. It was based on a well-designed campaign, on a facilitating audience, and a facilitating health system using an organized screening.
Choosing a social media platform adapted to the target is a major concern for a successful campaign. Twitter is interesting as it is well suited for appointment campaigns such as #SmearForSmear or the ALS Ice Bucket Challenge. It also is a social media platform used by young adults to keep up in real time with news [
The impact of facilitators is to be studied. As previously shown, many Twitter users of this campaign did not engage in this campaign as they did not post sensitizing tweets. But they participated and helped broadcasting to their audience. Models such as Cara Delevingne also posted a selfie to support the campaign and to raise awareness among her millions of followers (8.5 million in May 2017) [
Our findings show a clear need for studies that are capable of automatically analyzing the data and extracting useful insights from the #SmearForSmear Twitter campaign. We propose the use of machine learning to tackle these challenges, and we suggest 3 perspectives for future directions. First, we plan to undertake a large-scale analysis using a collection of tweets that we are currently collecting since February 2017. This analysis will include the application of the Latent Dirichlet Allocation to extract the topics emerging from the discussions about the campaign, as well as the exploration of the linguistic style of the Twitter’ users [
Health campaigns on social networks may raise awareness of public health issues. Becoming viral is not an end in itself. Long-term effect of social media campaigns to raise people’s awareness of health conditions is to be evaluated. The ALS Ice Bucket Challenge has proven to be disappointing as after 2 years, the level of Web-related activities about ALS has remained practically the same as it was before the campaign [
amyotrophic lateral sclerosis
National Health Service
odds ratio
The authors would like to thank Gérard Bourrel, Professor at the University of Montpellier, for his help and wise advice reviewing this paper. They would also like to thank and congratulate Jo’s Cervical Cancer Trust for their work on this inspiring campaign and its participants for their commitment to combat cervical cancer. This study did not require ethics approval as the authors only used publically available Twitter content.
None declared.