The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
In 2009 Ginsberg et al. reported using Google search query volume to estimate influenza activity in advance of traditional methodologies. It was a groundbreaking example of digital disease detection, and it still remains illustrative of the power of gathering data from the internet for important research. In recent years, the methodologies have been extended to include new topics and data sources; Twitter in particular has been used for surveillance of influenza-like-illnesses, political sentiments, and even behavioral risk factors like sentiments about childhood vaccination programs. As the research landscape continuously changes, the protection of human subjects in online research needs to keep pace. Here we propose a number of guidelines for ensuring that the work done by digital researchers is supported by ethical-use principles. Our proposed guidelines include: 1) Study designs using Twitter-derived data should be transparent and readily available to the public. 2) The context in which a tweet is sent should be respected by researchers. 3) All data that could be used to identify tweet authors, including geolocations, should be secured. 4) No information collected from Twitter should be used to procure more data about tweet authors from other sources. 5) Study designs that require data collection from a few individuals rather than aggregate analysis require Institutional Review Board (IRB) approval. 6) Researchers should adhere to a user’s attempt to control his or her data by respecting privacy settings. As researchers, we believe that a discourse within the research community is needed to ensure protection of research subjects. These guidelines are offered to help start this discourse and to lay the foundations for the ethical use of Twitter data.
The growing popularity of social media sites presents a unique opportunity to study human interactions and experiences. Twitter, one of the most popular social media sites, allows users to ‘microblog’ by sharing 140 character messages with their social network. Although Twitter doesn’t disclose the number of people who use its service, estimates are in the hundreds of millions and perhaps as many as half a billion1. Approximately 340 million tweets are sent every day around the world2. Researchers have begun to use this data to answer questions in a variety of fields3–9. Recent reflections on the data collection practices of the US National Security Administration have spurred similar meditations on the ethics of digital research10. The concern is that Twitter data could conceivably be used in a way that violates the privacy and rights of the tweet authors.
Twitter data have already been used in a number of studies to detect influenza-like illness (ILI)3–6, risky behaviors associated with the transmission of HIV7, sentiments about childhood vaccination programs8, and political sentiments9. These study designs generally feature count data, rather than user-specific data. For example, there are multiple studies that compare the proportion of tweets about flu-related symptoms to public health data on influenza-like incidence (ILI). An increase in syndromic flu tweets might indicate that an outbreak is occurring. These data are usually reported either at the national level or without any geographic parameters. Another common study design aims to determine public sentiments by counting words, phrases, and emoticons that co-occur with keywords like ‘Obama’. These sentiment indicators can be used to infer public opinions about political elections, mental health, or consumer products.
Unlike Facebook, Pinterest and other competitors, Twitter provides several application programming interfaces (APIs) that allow real-time access to vast amounts of content. Data streamed through the APIs include metadata about the authors, including the text location from their profile (e.g. ‘Baltimore’), their time zone, the time they sent the tweet, the number of friends and followers they have, the number of tweets they have ever sent, and more (Figure 1). Approximately 1% of tweets have a geolocation, which uses GPS to append the author’s precise geographic coordinates to the tweet. Geolocations are sufficiently detailed to determine from which wing of a building a tweet was sent. The default privacy settings do not enable geolocation, but do make a user's tweets and metadata available through the API. Users can modify their setting to make their profile private, which shields their account from public view online and from the API11.
There are numerous ways to access the data through these APIs: one method is through the ‘garden hose’, which is a random sample of approximately 1% of all live-streamed tweets. Other access methods include the search API which enables searching for particular users, hashtags, or locations, and author-specific queries which can retrospectively gather up to 3,200 tweets from a single user11. Furthermore, in 2010 Twitter donated its entire historical record of tweets to the US Library of Congress. Detailed plans for these data are not yet available, but the Library of Congress has indicated that it intends to collaborate with academic institutions to make the data available to researchers12.
The strength of tweets as a data source is in the volume; collection through the garden hose API brings in approximately 60,000–100,000 tweets per day. However because tweets are short and often lack context, it is difficult for computers to determine tweet content automatically. For this reason, researchers primarily use tweet data to conduct population-level research concerned with trends and patterns. Study designs rely on large volumes of data to accommodate false positives and negatives. A typical data set contains millions of tweets and many thousands of tweet authors. However, a user-centric use case involving Twitter is not inconceivable. Researchers interested in social network analysis, qualitative research, and rare-event topics may eventually turn to Twitter as a data source. Potential methodologies include building a social network out of @mentions (the @ is Twitter lexicon for referencing another user); mining qualitative data from specific user’s accounts; or conducting prospective research by following a person or small group of people over time. These user-centric approaches are fundamentally different from population-level studies, and may require different ethical considerations than aggregated study designs. Additional methodologies might also involve interacting with Twitter users, which will not be addressed here.
Under non-digital circumstances, ethics guidelines suggest that collecting information from a public space where people could ‘reasonably expect to be observed by strangers’ is considered appropriate even without informed consent13. According to these guidelines, tweets are text that users publish for the purpose of sharing with others. The weakness of this argument is that it fails to distinguish between population-level research and research focused on selected individuals. It would be clearly unethical for a researcher to follow one specific shopper around the mall and gather data exclusively about him without his consent. However, simply counting or observing behavior in aggregate at a mall is an acceptable research practice. The difference is that the latter example adheres to a level of privacy that the observed individual might expect from being in public, whereas the former violates those natural privacy boundaries. A similar distinction is needed in digital research.
As an example of the potential privacy pitfalls of digital research, suppose investigators were interested in the social networks of adolescents suffering from depression. A research plan might look like this: the investigators gather geocoded tweets that contain words relevant to the topic of interest, as shown in Figure 2. They filter for geocodes that correspond to school locations in order to identify adolescent users. From there, a simple query to the Twitter API returns a list of followers for each of those presumably depressed adolescents. They now have a social network. For each member of the network, they mine the user's tweet histories to find identifying details such as their real names. The researchers then use the gathered information to 'snowball' data collection by curating from a variety of different sources like Facebook, tumblr and the White Pages. They can collect birth dates, cell phone numbers, home addresses, favorite hangout spots, “likes” and “dislikes”, etc. The final result would be detailed demographic information for potentially thousands of people who exhibit symptoms of depression or are connected to a depressed adolescent. Current guidelines do not prohibit this kind of research activity. However, if the same information were collected through surveys or other traditional means, Institutional Review Board (IRB) approval would be needed.
The US Consumer Privacy Bill of Rights (CPBR)17 may instead serve as a useful framework for guiding researchers conducting research with Twitter. The CPBR was issued by the Obama administration in February 2012 in order to “give consumers clear guidance on what they should expect from those who handle their personal information, and set expectations for companies that use personal data.” There are seven principles enumerated by CPBR in Table 1.
Proposed guidelines for the ethical use of Twitter data
The objectives, methodologies, and data handling practices of the project are transparent and easily accessible
This information should be published in manuscripts, published on the web for the public to access, and provided to IRB (when relevant). Going forward, collaboration between the research community and Twitter to provide information to users about ongoing research and relevant results may also be beneficial. Transparency regarding uses of Internet data for research purposes is needed for fostering ‘privacy literacy’ so that the users can make informed decisions about participating in Twitter.
Study design and analyses respect the context in which a tweet was sent
A tweet author discussing his mental health, for example, does not do so with the intention of sharing that data with researchers; he does it to communicate with his digital community. Qualitatively analyzing these communications as if they are offered for research consumption does not align with the context in which the tweets were created. Twitter participants can reasonably expect to rely on some anonymity of the crowd to manage privacy.
The anonymity of tweet authors is protected, ensuring that subjects should not be identifiable in any way
Tweet data are not used to harvest additional information from other sources
Focused collection is also important for preserving anonymity. It is possible to use data collected from Twitter to discern the identities of tweet authors, which can then be used to find and collect additional information from additional sources. For example an author’s username, identifying details provided in tweet texts, or geolocations could all be used to collect data about that individual from other sources like Facebook, LinkedIn, Flickr, or public records.
Twitter users’ efforts to control their personal data are honored
Researchers may not follow a user on Twitter in order to gain access to a protected account. Doing so would violate that user’s efforts to control his or her personal data.
Researchers work collaboratively with IRB just as they would for any other human subject data collection
There is not currently an expectation that researchers engaging in research using Twitter will interface with their IRB. As discussed above, studies that could be conceived as individual-based should require IRB approval, whereas research designs that use data in aggregate (e.g. counts of keywords) may proceed without explicit consent. In turn, review boards should keep abreast of social network mining methodologies and corresponding ethical considerations in order provide informed guidance to researchers.
Research involving Twitter is growing in popularity, but the issues surrounding the ethics of using it as a data source have not yet been closely examined. There are hypothetical study designs that could use Twitter data in a way that violates the privacy and ethical treatment of participants. In order to avoid those misuses, six guidelines derived from the US Consumer Privacy Bill of Rights are proposed. We welcome discourse in the research community on this topic, and encourage further discussion.
Please use the #EthicalTwitter hashtag on Twitter to participate in this discussion online.
Open Peer Review
Current Referee Status:
Key to Referee Statuses
ApprovedThe paper is scientifically sound in its current form and only minor, if any, improvements are suggested
Approved with reservationsKey revisions are required to address specific details and make the paper fully scientifically sound
Not approvedFundamental flaws in the paper seriously undermine the findings and conclusions
Read more about the unique F1000Research publication and peer review model here.
Competing Interests Policy
Provide sufficient details of any financial or non-financial competing interests to enable users to assess whether your comments might lead a reasonable person to question your impartiality. Consider the following examples, but note that this is not an exhaustive list:
- Within the past 4 years, you have held joint grants, published or collaborated with any of the authors of the selected paper.
- You have a close personal relationship (e.g. parent, spouse, sibling, or domestic partner) with any of the authors.
- You are a close professional associate of any of the authors (e.g. scientific mentor, recent student).
- You work at the same institute as any of the authors.
- You hope/expect to benefit (e.g. favour or employment) as a result of your submission.
- You are an Editor for the journal in which the article is published.
- You expect to receive, or in the past 4 years have received, any of the following from any commercial organisation that may gain financially from your submission: a salary, fees, funding, reimbursements.
- You expect to receive, or in the past 4 years have received, shared grant support or other funding with any of the authors.
- You hold, or are currently applying for, any patents or significant stocks/shares relating to the subject matter of the paper you are commenting on.