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Use of social media big data as a novel HIV surveillance tool in South Africa

  • Alastair van Heerden ,

    Roles Conceptualization, Writing – review & editing

    Affiliations Human and Social Development, Human Sciences Research Council, Pietermaritzburg, KwaZulu Natal, South Africa, Developmental Pathways for Health Research Unit, University of the Witwatersrand, Johannesburg, Gauteng, South Africa

  • Sean Young

    Roles Writing – review & editing

    Affiliations Department of Informatics, University of California Institute for Prediction Technology (UCIPT), University of California Irvine, Irvine, CA, United States of America, Department of Emergency Medicine, University of California, Irvine, CA, United States of America


Sub-Saharan Africa has been heavily impacted by the HIV/AIDS epidemic. Social data (e.g., social media, internet search, wearable device, etc) show great promise assisting in public health and HIV surveillance. However, research on this topic has primarily focused in higher resource settings, such as the United States. It is especially important to study the prevalence and potential use of these data sources and tools in low- and middle-income countries (LMIC), such as Sub-Saharan Africa, which have been heavily impacted by the HIV epidemic, to determine the feasibility of using these technologies as surveillance and intervention tools. Accordingly, we 1) described the prevalence and characteristics of various social technologies within South Africa, 2) using Twitter, Instagram, and YouTube as a case study, analyzed the prevalence and patterns of social media use related to HIV risk in South Africa, and 3) mapped and statistically tested differences in HIV-related social media posts within regions of South Africa. Geocoded data were collected over a three-week period in 2018 (654,373 tweets, 90,410 Instagram posts and 14,133 YouTube videos with 1,121 comments). Of all tweets, 4,524 (0.7%) were found to related to HIV and AIDS. The percentage was similar for Instagram 95 (0.7%) but significantly lower for YouTube 18 (0.1%). We found regional differences in prevalence and use of social media related to HIV. We discuss the implication of data from these technologies in surveillance and interventions within South Africa and other LMICs.


Ever increasing sources of digital data relevant to health, inexpensive storage, and new techniques for analysing these data offer an incredible opportunity to improve human health and wellbeing [13]. National Health Systems and ongoing population surveillance continue to improve as new sources of data come online. Some of the most promising technologies and approaches may include the internet of things (IoT) and cyberphysical systems (CPS), digital phenotyping through user–phone interactions, and user generated data on large social platforms such as Facebook, Instagram and Twitter [4]. These emerging “social data” sources may radically change the granularity, volume, velocity, and variety of patient health and wellness data [5]. In the next 10 years, taking these heterogenous data made up of highly dimensional sparse data and applying advanced statistics methods and novel computational approaches to machine/artificial intelligence could result in a transformation in biomedical and behavioural research.

A useful roadmap to frame this journey was proposed by Bardhan et al. [6]. It foregrounds the importance of connection, in particular of people, systems and data. They envisage a transition from provider-centred practices to patient-centred care facilitated through online health communities, artificial intelligence and sensor data. The authors note that the systems supporting population health will require the integration of multiple sources of data, such as physiological measures, genomic biomarkers, health record and potentially social media content. Finally, the connected data produced by connecting people and systems will allow diagnostic advances to be made using data dependant analytic approaches.

This plan looks exciting and feasible in high-income countries (HICs), but low- and middle-income countries (LMICs) lag behind HIC with undeveloped health systems that struggle under weight of enormous burden of disease. Further, advances made in HIC do not automatically translate through a simple process of adaption into tools that are readily useable by LMICs. For many parts of the world the biggest challenge in public health epidemiology is not connecting people, systems and data but the dearth of accurate and up to date surveillance data. Collecting, cleaning, analyzing, and making available reliable data which can be used for evidence-based planning, decision and policy making is still the priority in many parts of the world. Traditional approaches to disease surveillance lean heavily on established infrastructure and a network of data provides for reporting. The approach is costly and can result in significant delays between data capture and reporting while further burdening already strained healthcare infrastructure [7]. Without the financial resources or human capacity available in high income countries, achieving similar levels of health and disease surveillance in low- and middle- income countries (LMIC) will require novel solutions that leapfrog the public health surveillance infrastructure that is used for decision making in more well-resourced countries.

For example, rather than relying on traditional antennal clinic data to estimate HIV prevalence, another possible solution would be to explore whether the vast quantities of data generated by people (i.e., user-generated data) through their use of information and communication/social technologies might be harnessed for public health surveillance [8, 9]. These data can be broadly broken down into two primary domains, namely user content generated through active engagement and device generated data which can be passively collected. Three broad areas of user generated content can be identified. First, interacting with and posting to platforms such as Facebook, Twitter or Twitch produces information both from the content of the post (which may include text, images and/or video) as well as about social networks, user location and other paradata. Second, people produce vast amounts of data through smartphone messaging applications. These data could come either from carrier operated services such as Short Messaging Service (SMS) or through the use of messaging applications, e.g. iMessanger, WhatsApp and WeChat. Finally, user generated searches on e-commerce sites and search engines offer a third category of user generated digital data.

Passively generated data, descriptively referred to as a digital exhaust, are the data produced as a by-product of human computer interactions [10]. Examples of such data include reviewing the time a phone screen is first turned on in the morning and last turned off at night to give a sense of daily routine and hours of sleep or converting accelerometer data into a measure of daily activity [11, 12]. As devices become more deeply embedded in the environment, passive data generated by mobile phones and wearable devices will be supplemented by, for example, devices monitoring air quality, food consumed and/or time spent commuting by foot, car or public transport. Data produced by such “smart cities” may supplement data passively produced by body worn and body carried devices for health surveillance [13, 14].

A number of studies have been conducted looking at the prevalence of use of these digital data, ways that these data can be used to predict health outcomes, and ways the insights from these data can potentially be implemented by researchers and health departments [9, 1518]. However, this work has largely been conducted in HIC, rather than LMIC, where these innovative low-cost solutions may be most needed [8, 19]. Of the two approaches (user generated content versus passive data), user generated content may offer more immediate opportunities in LMIC. Due to limited fixed line infrastructure, much of the African continent has seen rapid and widespread adoption of mobile phones [20]. The United Nation’s International Telecommunication Union (ITU) report that 4 out of every 5 people in the least-developed countries (LDCs) of the world now have access to mobile-cellular networks. While internet use remains low, around 25% of people in LDCs are expected to be online in 2020. In the more well developed African countries of South Africa (54%), Kenya (85%) and Nigeria (50%), internet use is much higher, coming close to the world average when Africa is excluded (58%) [21]. The availability of mobile phones in LMIC has seen a rapid body of evidence accumulate in the field of mHealth [2225]. Of the mHealth literature focused on social media, to date, attention has been on its use as a tool for health promotion [2628]. Two recent reviews of studies undertaken to assess the use of social media for disease surveillance suggest that while the current evidence base has may gaps, biases and a focus on high income countries, strengths of the approach, such as effectiveness and rapid detection of disease, suggest that further exploration is warranted [29, 30].

In this paper we aim to contribute to the literature by exploring the feasibility of using data from social technologies (with social media data as a case study) for monitoring health discussions in South Africa. Due to the burden of HIV and AIDS in South Africa we focused both on the overall number of posts made within South Africa, as well as those specific to different HIV-related topics. Little known research has explored the potential of these data in LMIC’s. This is also the first study we are aware of that explores this topic using YouTube and Instagram (which are some of the most prevalent technologies used among youth, who are at high-risk for HIV) as sources of data for public health surveillance in LMIC.


We aimed to assesses the feasibility of our approach by assimilating a comprehensive list of potential data sources that might be used for monitoring HIV and AIDS health related discussions. We then select, as examples, three social media platforms that have large user bases in South Africa (Twitter, Instagram and YouTube) as case studies for the proposed health surveillance approach. To establish the primary social data sources available in South Africa, along with information as to the penetration and availability of data for these sources we 1) conducted a non-systematic review of the literature, and 2) undertook personal communication with the local market research companies responsible for producing the South African Social Media Landscape Report 2017 [31].


Twitter is an Internet-based social networking and microblogging service that permits registered users to receive and broadcast very short text messages (tweets), up to 140 characters in length. Users and collective sites are identified by user names and hashtag identifiers such as “#tgif”. Messages can be resent or forwarded (retweeted), and groups of associates can associate together (as friends) in sending and receiving group tweets. As of May 2018, Twitter provides three services by which one can gain programmatic access to its global database of Tweets [32]. Firehose access is a paid for service provided by the company to select partners and is a complete export of all Tweets matching the provided filter criteria. The other two methods available are connection either to the streaming application programming interface (API) or the Search API. The streaming API returns in real-time a maximum of 1% of the total tweet volume currently available through the firehose [33]. In contrast, the Search API allows queries against an index of recent Tweets and can include filters that limit search to particular users, words and locations. The Search API is rate limited to allow only 180 queries every 15 minutes [32]. For this study the search API was used to retrieve and store Tweets. Tweets were collected in compliance with the terms of service in existence at the time. In order to perform a geographically constrained search the API, the request is accompanied by latitude, longitude and radius.[34] Using a latitude of -28.149503 and longitude of 24.543457 with a radius of 931 kilometers (578.5 miles) it was possible to construct a bounding circle that covered the entire country. Fig 1 depicts this strategy used to proximally limit Tweets to those originating from South Africa. The search API uses a two-stage process when requested to conduct a geo search.

Fig 1. Search strategy used to identify tweets originating in South Africa.


Instagram is a popular photo and video-sharing social networking service. Methods for interfacing with the Instagram API are much scarcer in the literature. Where literature does exit e.g. Hochman and Schwartz [35], the focus of geospatial analysis is on only one or two cities. A primary reason for this is the Instagram API only allows for geocoded searches within a 5 kilometers radius of a given latitude and longitude. This is typically sufficient for a city but inadequate when wishing to investigate usage at a country level. Further complicating this approach was a tightening of the geosearch API during the course of this study due to the Cambridge Analytica scandal [36]. To overcome these limitations, we implemented a simple yet novel method for extracting the images for an entire country using Instagram location ids. These ids correlate to points of interest and media can be searched for in a 5-kilometer radius around the location id point. These ids are not publicly available but can be obtained through the API by searching for a place or place name. Using a list of South African city names, we obtained a list of 16,751 location ids. Due to rate limitations on the number of queries that can be processed, a random sample of roughly 10% of these location ids (1,411) were used to construct an Instagram API query using the Media/search endpoint to search for all posts within a 5 kilometer radius of this location. These data were collected in compliance with the terms of service in existence at the time.


Founded in 2007 as a video sharing site, YouTube has become the second most frequently visited site on the internet [37]. An API is publicly available that can be search by geolocation. Searches are restricted to return a maximum of 500 videos with a 5-kilometer radius of the provided latitude and longitude. The 500-video limit is a function of the vast library of content that is available and a statistical search strategy used by YouTube to return the most relevant results in the shortest amount of time possible. Using Quantum GIS 2.10 software[38], a vector shape file containing the boundary of South Africa[39] was loaded. A python script, available as part of the mmqgis plugin [40], was then used to generate a point grid that covered the entire extent of South Africa. The spacing of the grid points can be specified using either the project coordinate reference system (CRS) or absolute values (meters, kilometers, feet, miles). Generated points are aligned at even multiples of the provided values. Using this grid packing approach, a list of 46,220 candidate lat/long points were generated each spaced five kilometers from each other (Fig 2).

Fig 2. Subsection of the five kilometer point grid covering the entire extent of South Africa.

For each lat/long point generated by the packing algorithm, it was then possible to construct a YouTube API query to search for all videos within a 5-kilometer radius of this point. The id of each video retrieved was then used to search for and return all comments attached to the video. These data were collected in compliance with the terms of service in existence at the time.

Data processing

Access to each of the three social media sites was coded into python scripts and data stored in relational MySQL database. Keyword queries for topics related to HIV included the terms “HIV”, “AIDS”, “sex”, and “fuck”. A coding scheme was generated by the last author and all media related to these terms were coded by the middle author with the first author reviewing all coded media. Pseudo code for each of the Twitter, Instagram and Youtube python scripts are provided in S1 File.


Data were imported as comma separated files into SPSS v24. From these data we generated estimates of proportions, means, and confidence intervals. We then examined the data further through the use of descriptive statistics and test for differences across different geographic boundaries within South Africa and between Social Media platforms (Twitter, Instagram, YouTube) using correlations, chi-square, and analogous non-parametric tests for categorical variables, as appropriate. Consistent with the exploratory nature of this work and the relatively modest sample size, the majority of analyses relied on univariate and bivariate methods described above. Statistical significance was de-emphasized in favor of generating effect size estimates of promising associations that will be further explored in subsequent studies. Geocoded social media data were imported into QGIS 3.2 and spatial analyses undertaken. The non-parametric single variable chi-square was used to compare social media use with reference to provincial population distribution with the null hypothesis being that social media use would be equally distributed across the provinces.


The literature, tweets, YouTube videos and comments and Instagram posts used in this study are all publicly available and accessible to anyone with a freely available API access key. Consent for the tweet and post data to be read is secured by both companies when users sign the Twitter, YouTube and Instagram Terms and Agreements. These documents make it clear that users are agreeing to public privacy settings. This project was exempted from IRB approval as it does not meet the criteria for human subjects’ research. For the purposes of this study, user ID, names and emails were not collected or stored.


The results of the search for data sources that could be used to monitor HIV dialog for health surveillance in South Africa are presented in Table 1. Facebook had the largest number of users with 16 of the 18 million active social media users on this platform [41]. Access to geocoded data varied from simple access on platforms such as YouTube to no access (e.g. WhatsApp). A further distinction was the level of search functionality provided. Geocoded search to all media was available on platforms such as Twitter while other platforms, such as LinkedIn, limited search to known user profiles.

Using the methods described above for the three case study platforms a three-week period of data collection was undertaken for each starting on May 21st and ending on the 8th June 2018. After cleaning the raw data returned from the three platforms API and removing retweets (repeat posts that contain no new content) and duplicate posts 654,373 tweets, 90,410 Instagram posts and 14,133 YouTube videos with 1,121 attached comments remained. Of this total 29,920 (4.6%) tweets were found to originate from points outside the boundary of South Africa. Table 2 presents these data along with a disaggregation by South African province. For comparison the most recent population estimates from the South African National Statistics council are also included. It should be noted that all data represent only a random sample of media produced on each platform during the collection period.

Table 2. Totals of data collected from Twitter, Instagram and YouTube between May 21, 2018 and June 8, 2018 by province.

Social media post counts related to HIV and AIDS, retrieved using a simple keyword search, are presented in Table 3. Posts were classified using a simple coding scheme that targeted differences in media related to Risk, Risk Reduction and News / Research. These results were further disaggregated by media put out by civil society vs unaffiliated individuals. Of the 624,451 tweets, 0.7% were found to related to HIV and AIDS. The percentage was similar for Instagram (0.7) but significantly lower for YouTube (0.1%).

Table 3. Sexual risk and HIV related terms extracted from Twitter, Instagram and YouTube.

Looking at the distribution of tweets across categories reveals that NGO’s use of Twitter is significantly associated with topic (χ2 (3) = 128.9, Cramer’s V = 0.66). It appears that information sharing about the latest news and research findings is the most common use of Twitter among NGOs. For private accounts the picture is somewhat different with discussions about risk dominating the discourse. Results suggest a substantive relationship between topic and private use (χ2 (3) = 65.8, Cramer’s V = 0.47). While counts were too low to test for association in the YouTube and Instagram NGO data, a significant association was identified in the type of topic discussed by private users on Instagram (). As with Twitter, the majority of posts were related to topics of HIV risk (χ2 (3) = 170.0, Cramer’s V = 0.75).

Figs 3 and 4 illustrate the geographic spread of all social media post across South Africa in the three-week period (Fig 3) and for HIV related terms only (Fig 4). Compared to the provincial population proportions we found expected counts from all three social media platforms to vary significantly in their distribution. YouTube (χ2 (8) = 6138.0, p < 0.01), Instagram (χ2 (8) = 10681.2, p < 0.01), and Twitter (χ2 (8) = 4783.7, p < 0.01) were all proportionally over represented in the Western Cape. The province also has the lowest unemployment rate (20.9%) in the country (29.1%), is well known as an important tourist destination and count technology start-ups, call centres, advertising and TV production among their most important industries [42]. Twitter and YouTube are over represented in Gauteng which is the economic capital of the country, while Instagram is over represented in the North West province which is home to some of the country’s best wild life reserves. Instagram posts also overlay with the road network suggesting people taking photos while on road trips. A similar disproportionate distribution of posts was found across the three social media platforms in relation to provincial population distribution. Gauteng and the Western Cape accounted for 35.3% of the population and 66.1% of all Instagram, 59.4% Twitter and 8 of the 18 YouTube videos and posts.

Fig 3. Map of South Africa showing distribution of Twitter, Instagram and YouTube social media post over a three-week period with road network overlaid.

Fig 4. Map of South Africa showing distribution of Twitter, Instagram and YouTube social media post related to HIV over a three-week period.


Twitter, Instagram and YouTube were all found to have a significant number of active users in South Africa. While the majority of posts emanate from users in the large metros of Cape Town, Johannesburg and Durban, activity is visible across the country. This is particularly true of Twitter, found to be the most popular of the three platforms, and Instagram. Over half of all YouTube activity is isolated to the Western Cape and Gauteng. YouTube comments are unexpectedly skewed towards the Eastern Cape. Investigation suggests that the reason behind this distribution is that a video title “We can't go on like this | South Africa” received 867 comments. The video dealt with Racism in South Africa. A number of tweets were found to reference content produced outside of South Africa. The reason for these data anomalies was the search strategy used to collect tweets. A single central latitude and longitude was use and all tweets within a 931km radius stored. This meant that the dataset included tweets from neighboring countries including Botswana, Namibia and Mozambique.

Due to the burden of disease posed by HIV and AIDS, the study focused on the presence of social media posts that directly reference the disease or allude to sexual activity. Using a simple keyword search strategy under 1% of posts across all three platforms were found to be relevant. A more nuanced approach may yield better results. For the posts that were found, it becomes clear that civil society is currently only engaging with the South African community though Twitter. Around 61% of all tweets were research or news articles tweeted by Non-Governmental Organizations (NGO). Only a single Instagram post from the mothers2mothers NGO relating to HIV and AIDS was found on Instagram and no YouTube videos could be identified as being posted by similar groups. These findings suggest a clear opportunity exists for NGOs and civil society to move some of their advocacy and engagement work onto these two other platforms. Due to its size, Instagram should be prioritized. When considering the textual content of media posted by private users, sexually risky behaviors is by far the most common classification. Between 54% and 81% of posts across the three platforms fall into this category. Only a small percent (2.1%-7%) related to safer sex practices. These findings suggest that public health messages may not yet have altered people’s perceptions of HIV and the risk associated with less safe sex practices.

Connecting people

We found that how NGOs use these three platforms to connect with people is very different from how people use the platforms to connect with each other. First, although across all platforms, Twitter had the highest frequency of posting, within platforms, the most frequent posts were “risk-related” posts, such as individuals describing their interests and/or intentions to engage in sexual risk behaviors. Further research can explore the potential of social media posts about risk to identify trends in risk and improve surveillance. Relatedly, within Twitter, NGO’s are primarily using Twitter to discuss news and risk-reduction (e.g., importance of using condoms), while individuals are using it to discuss risk. Understanding how different groups and individuals use social media sites can help inform HIV and health promotion efforts more broadly, as well as help HIV and public health outreach organizations better understand which social media sites are already saturated in order to turn their approaches to new technologies and methods.

Connecting with data

Working with social media data can be challenging due to data volume and storage, velocity of data creation, the variety of social media data that exists and uncertainty about data quality [43]. Similar issues were encountered in this study with data volume and storage quickly exceeding the funds set aside for the study. A document, rather than relational database was used to successfully manage the unstructured nature of the collected data and traditional exploratory data analysis was found adequate for identifying missing, incomplete or outlier data. The challenge working with these unstructured data is magnified when looking to link data with structured electronic health records. Standards such as the Fast Healthcare Interoperability Resources (FHIR) provide a modern scaffolding for exchanging healthcare information electronically [44].

While access is more difficult due to a lack of a public API, we found that Whatsapp and Facebook were the two largest social media platforms in use in South Africa. Whatsapp has functionality which enables individuals to export chats as raw text. This is potentially an interesting additional source of data and might support further understanding of how health related risks and behaviors are discussed within a peer network. Despite the promise these and other social media data sources hold, it is an ongoing debate as to if, and how best these data can support the greater good while respecting the individuals rights to privacy and anonymity [45].

Connecting with systems

The value of these non-traditional data sources to complement health systems data is becoming increasingly obvious, the National Institutes of Health initiative focusing on Data Science for Health Discovery and Innovation in Africa Research Hubs, being one example [46]. They note that infrastructure, specifically mobile phone networks, continue to grow rapidly in Africa offering a valuable opportunity to the continent to explore new sources of health data with contextually relevant ethical, legal and social implications key research areas. While the ethical issues with using social media data are becoming more well understood, for instance privacy, anonymity, and consent, their application to populations with lower level technological health literacy requires attention [47, 48].


The primary limitation of this study was the short period of time the social network data were collected. Funding constraints prevented data being collected for a longer period of time. Const associated with this type of data collection include the downloading and storage of large amounts of data. It is not clear how representative the months’ worth of collected data is of general usage trends and an extended data collection period would be an essential next step. A second limitation, related more to the approach than to this particular study, are the biases inherent in different forms of health data. For example, what people search for, official CDC statistics and what the media presents in terms of health and disease are likely to produced different results. Finally, we used a coding scheme to identify topics discussed on social media. This was a primitive initial method to explore differences in topics, however, more advanced methods of identifying topics and discussions are available and should be explored.

Future directions

Image and video data collected from Instagram and YouTube present a novel opportunity for public health research. Future work should consider how best these audio-visual data could be analyzed to provide information on the health of the population. Many of the posted Instagram images include either people and/or food. Together they may offer an interesting approach to developing National models of food, alcohol and sugary drink consumption and estimates of Overweight and Obesity. Comparing National HIV surveillance data to the spread of social media posts could also yield information about the usefulness of these data for HIV surveillance and intervention. Another promising direction this work could take would be to explore the possibility of associating social media data with longitudinal data collected at country level demographic surveillance sites (DSS). With only a small additional burden on existing DSS data collection systems, it might be possible to supplement longitudinal health records of individuals living in the area under surveillance with social media data which might provide a different perspective and insight into the health risks, behaviors and conversations people are having about health thought their online profiles.


Smartphones, the social media platforms people access on these devices and the content they create all hold potential as sources of health data. While feasible to collect, there is still much work required to create models that are unbiased, accurate and useful as tools for health surveillance in low and middle-income countries.


  1. 1. Ma Y, Wang Y, Yang J, Miao Y, Li W. Big Health Application System based on Health Internet of Things and Big Data. IEEE Access. 2017;5: 7885–7897.
  2. 2. Mehta N, Pandit A. Concurrence of big data analytics and healthcare: A systematic review. Int J Med Inform. 2018;114: 57–65. pmid:29673604
  3. 3. Beam AL, Kohane IS. Big Data and Machine Learning in Health Care. JAMA. 2018;319: 1317. pmid:29532063
  4. 4. Agarwal R, Dugas M, Gao G (Gordon), Kannan PK. Emerging technologies and analytics for a new era of value-centered marketing in healthcare. J Acad Mark Sci. 2020;48: 9–23.
  5. 5. Ishwarappa Anuradha J. A Brief Introduction on Big Data 5Vs Characteristics and Hadoop Technology. Procedia Comput Sci. 2015;48: 319–324.
  6. 6. Bardhan I, Chen H, Karahanna E. Connecting systems, data, and people: A multidisciplinary research roadmap for chronic disease management. MIS Ouarterly. 2020;44: 185–201.
  7. 7. Ao TT, Rahman M, Haque F, Chakraborty A, Hossain MJ, Haider S, et al. Low-Cost National Media-Based Surveillance System for Public Health Events, Bangladesh. Emerg Infect Dis. 2016;22: 720–722. pmid:26981877
  8. 8. Young SD, Mercer N, Weiss RE, Torrone EA, Aral SO. Using social media as a tool to predict syphilis. Prev Med (Baltim). 2018;109: 58–61. pmid:29278678
  9. 9. Young SD, Torrone EA, Urata J, Aral SO. Using Search Engine Data as a Tool to Predict Syphilis. Epidemiology. 2018;29: 574–578. pmid:29864105
  10. 10. Estrin D. Sensemaking for mobile health. Proceedings of the 12th international conference on Information processing in sensor networks—IPSN ‘13. New York, New York, USA: ACM Press; 2013. p. 1.
  11. 11. Estrin D. Small data, where n = me. Commun ACM. 2014;57: 32–34.
  12. 12. Yang C-C, Hsu Y-L. A Review of Accelerometry-Based Wearable Motion Detectors for Physical Activity Monitoring. Sensors. 2010;10: 7772–7788. pmid:22163626
  13. 13. Kamel Boulos MN, Al-Shorbaji NM. On the Internet of Things, smart cities and the WHO Healthy Cities. Int J Health Geogr. 2014;13: 10. pmid:24669838
  14. 14. Mora H, Gil D, Terol RM, Azorín J, Szymanski J. An IoT-Based Computational Framework for Healthcare Monitoring in Mobile Environments. Sensors. 2017;17: 2302. pmid:28994743
  15. 15. McLaughlin ML, Hou J, Meng J, Hu C-W, An Z, Park M, et al. Propagation of Information About Preexposure Prophylaxis (PrEP) for HIV Prevention Through Twitter. Health Commun. 2016;31: 998–1007. pmid:26756069
  16. 16. Adrover C, Bodnar T, Huang Z, Telenti A, Salathé M. Identifying Adverse Effects of HIV Drug Treatment and Associated Sentiments Using Twitter. JMIR Public Heal Surveill. 2015;1: e7. pmid:27227141
  17. 17. Johnson AK, Mikati T, Mehta SD. Examining the themes of STD-related Internet searches to increase specificity of disease forecasting using Internet search terms. Sci Rep. 2016;6: 36503. pmid:27827386
  18. 18. Young SD. Behavioral insights on big data: using social media for predicting biomedical outcomes. Trends Microbiol. 2014;22: 601–602. pmid:25438614
  19. 19. Young SD, Zhang Q. Using search engine big data for predicting new HIV diagnoses. Nishiura H, editor. PLoS One. 2018;13: e0199527. pmid:30001360
  20. 20. van Heerden AC, Norris SA, Richter LM. Using Mobile Phones for Adolescent Research in Low and Middle Income Countries: Preliminary Findings From the Birth to Twenty Cohort, South Africa. J Adolesc Heal. 2010;46: 302–304. pmid:20159510
  21. 21. UN Office of the High Representative for the Least Developed Countries LDC and SIDS (UN-O. ICTs, LDCs and the SDGs: Achieving universal and affordable Internet in the least developed countries. Geneva, Switzerland; 2018.
  22. 22. Lee SH, Nurmatov UB, Nwaru BI, Mukherjee M, Grant L, Pagliari C. Effectiveness of mHealth interventions for maternal, newborn and child health in low–and middle–income countries: Systematic review and meta–analysis. J Glob Health. 2016;6. pmid:26649177
  23. 23. Stephani V, Opoku D, Quentin W. A systematic review of randomized controlled trials of mHealth interventions against non-communicable diseases in developing countries. BMC Public Health. 2016;16: 572. pmid:27417513
  24. 24. Barron P, Pillay Y, Fernandes A, Sebidi J, Allen R. The MomConnect mHealth initiative in South Africa: Early impact on the supply side of MCH services. J Public Health Policy. 2016;37: 201–212. pmid:27899795
  25. 25. Hamine S, Gerth-Guyette E, Faulx D, Green BB, Ginsburg AS. Impact of mHealth Chronic Disease Management on Treatment Adherence and Patient Outcomes: A Systematic Review. J Med Internet Res. 2015;17: e52. pmid:25803266
  26. 26. Cao B, Gupta S, Wang J, Hightow-Weidman LB, Muessig KE, Tang W, et al. Social Media Interventions to Promote HIV Testing, Linkage, Adherence, and Retention: Systematic Review and Meta-Analysis. J Med Internet Res. 2017;19: e394. pmid:29175811
  27. 27. Neiger BL, Thackeray R, Van Wagenen SA, Hanson CL, West JH, Barnes MD, et al. Use of Social Media in Health Promotion. Health Promot Pract. 2012;13: 159–164. pmid:22382491
  28. 28. Korda H, Itani Z. Harnessing Social Media for Health Promotion and Behavior Change. Health Promot Pract. 2013;14: 15–23. pmid:21558472
  29. 29. Charles-Smith LE, Reynolds TL, Cameron MA, Conway M, Lau EHY, Olsen JM, et al. Using Social Media for Actionable Disease Surveillance and Outbreak Management: A Systematic Literature Review. Braunstein LA, editor. PLoS One. 2015;10: e0139701. pmid:26437454
  30. 30. Bernardo TM, Rajic A, Young I, Robiadek K, Pham MT, Funk JA. Scoping Review on Search Queries and Social Media for Disease Surveillance: A Chronology of Innovation. J Med Internet Res. 2013;15: e147. pmid:23896182
  31. 31. World Wide Worx, Ornico. SA Social Media Landscape 2017. Johannesburg; 2017.
  32. 32. Twitter Search API.
  33. 33. Burton SH, Tanner KW, Giraud-Carrier CG, West JH, Barnes MD. “Right Time, Right Place” Health Communication on Twitter: Value and Accuracy of Location Information. J Med Internet Res. 2012;14: e156. pmid:23154246
  34. 34. Young SD, Rivers C, Lewis B. Methods of using real-time social media technologies for detection and remote monitoring of HIV outcomes. Prev Med (Baltim). 2014;63: 112–115. pmid:24513169
  35. 35. Hochman N, Schwartz R. Visualizing Instagram: Tracing Cultural Visual Rhythms. Sixth International AAAI Conference on Weblogs and Social Media. Dublin, Ireland: AAAI Publications; 2012.
  36. 36. Tarran B. What can we learn from the Facebook-Cambridge Analytica scandal? Significance. 2018;15: 4–5.
  37. 37. Burgess J, Green J. YouTube: Online Video and Participatory Culture. New York City, United States: John Wiley & Sons, Ltd.; 2013.
  38. 38. QGIS Development Team. QGIS Geographic Information System. Open Source Geospatial Foundation Project; 2015.
  39. 39. Map Library—South Africa.
  40. 40. Minn M. mmqgis plugin. 2015.
  41. 41. Hootsuite. Digital in 2018: Essential insights into internet, social media, mobile, and ecommerce use around the world. 2018.
  42. 42. Statistics South Africa. Quarterly Labour Force Survey: Quarter 1, 2016. Pretoria, South Africa; 2016.
  43. 43. Stieglitz S, Mirbabaie M, Ross B, Neuberger C. Social media analytics–Challenges in topic discovery, data collection, and data preparation. Int J Inf Manage. 2018;39: 156–168.
  44. 44. Braunstein ML. Healthcare in the Age of Interoperability: The Promise of Fast Healthcare Interoperability Resources. IEEE Pulse. 2018;9: 24–27. pmid:30452344
  45. 45. Isaak J, Hanna MJ. User Data Privacy: Facebook, Cambridge Analytica, and Privacy Protection. Computer (Long Beach Calif). 2018;51: 56–59.
  46. 46. National Institutes of Health. Harnessing Data Science for Health Discovery and Innovation in Africa. In: Office of Strategic Coordination—The Common Fund [Internet]. 2020 [cited 25 May 2020]. Available:
  47. 47. Hunter RF, Gough A, O’Kane N, McKeown G, Fitzpatrick A, Walker T, et al. Ethical Issues in Social Media Research for Public Health. Am J Public Health. 2018;108: 343–348. pmid:29346005
  48. 48. van Heerden A, Wassenaar D, Essack Z, Vilakazi K, Kohrt BA. In-Home Passive Sensor Data Collection and Its Implications for Social Media Research: Perspectives of Community Women in Rural South Africa. J Empir Res Hum Res Ethics. 2019; 155626461988133. pmid:31631742