Question: Can the demographic bias in sampling the data lead to exclusion of minority groups, resulting in overgeneralisation of models based on these data？
Evidence: Ethical considerations are established as essential part in planning mental health research and most research projects undergo approval by an ethics committee. On the contrary, the computational linguistics community has started only recently to consider ethical questions BIBREF72 , BIBREF73 . Likely, this is because computational linguistics was traditionally concerned with publicly available, impersonal texts such as newspapers or texts published with some temporal distance, which left a distance between the text and author. Conversely, recent social media research often deals with highly personal information of living individuals, who can be directly affected by the outcomes BIBREF72 .
 BIBREF72 discuss issues that can arise when constructing datasets from social media and conducting analyses or developing predictive models based on these data, which we review here in relation to our project: Demographic bias in sampling the data can lead to exclusion of minority groups, resulting in overgeneralisation of models based on these data. As discussed in the introduction, personal recovery research suffers from a bias towards English-speaking Western individuals of white ethnicity. By studying multilingual accounts of ethnically diverse populations we explicitly address the demographic bias of previous research. Topic overexposure is tricky to address, where certain groups are perceived as abnormal when research repeatedly finds that their language is different or more difficult to process. Unlike previous research BIBREF45 , BIBREF47 , BIBREF46 our goal is not to reveal particularities in the language of individuals affected by mental health problems. Instead, we will compare accounts of individuals with BD from different settings (structured interviews versus informal online discourse) and of different backgrounds. While the latter bears the risk to overexpose certain minority groups, we will pay special attention to this in the dissemination of our results.
Lastly, most research, even when conducted with the best intentions, suffers from the dual-use problem BIBREF74 , in that it can be misused or have consequences that affect people's life negatively. For this reason, we refrain from publishing mental health classification methods, which could be used, for example, by health insurance companies for the risk assessment of applicants based on their social media profiles.
If and how informed consent needs to be obtained for research on social media data is a debated issue BIBREF75 , BIBREF76 , BIBREF77 , mainly because it is not straightforward to determine if posts are made in a public or private context. From a legal point of view, the privacy policies of Twitter and Reddit, explicitly allow analysis of the user contents by third party, but it is unclear to what extent users are aware of this when posting to these platforms BIBREF78 . However, in practice it is often infeasible to seek retrospective consent from hundreds or thousands of social media users. According to current ethical guidelines for social media research BIBREF79 , BIBREF80 and practice in comparable research projects BIBREF81 , BIBREF78 , it is regarded as acceptable to waive explicit consent if the anonymity of the users is preserved. Therefore, we will not ask the account holders of Twitter and Reddit posts included in our datasets for their consent.
 BIBREF79 formulate guidelines for ethical social media health research that pertain especially to data collection and sharing. In line with these, we will only share anonymised and paraphrased excerpts from the texts, as it is often possible to recover a user name via a web search for the verbatim text of a post. However, we will make the original texts available as datasets to subsequent research under a data usage agreement. Since the (automatic) annotation of demographic variables in parts of our dataset constitutes especially sensitive information on minority status in conjunction with mental health, we will only share these annotations with researchers that demonstrate a genuine need for them, i.e. to verify our results or to investigate certain research questions.
Another important question is in which situations of encountering content indicative of a risk of self-harm or harm to others it would be appropriate or even required by duty of care for the research team to pass on information to authorities. Surprisingly, we could only find two mentions of this issue in social media research BIBREF81 , BIBREF82 . Acknowledging that suicidal ideation fluctuates BIBREF83 , we accord with the ethical review board's requirement in BIBREF81 to only analyse content posted at least three months ago. If the research team, which includes clinical psychologists, still perceives users at risk we will make use of the reporting facilities of Twitter and Reddit.
As a central component we consider the involvement of individuals with lived experience in our project, an aspect which is missing in the discussion of ethical social media health research so far. The proposal has been presented to an advisory board of individuals with a BD diagnosis and was received positively. The advisory board will be consulted at several stages of the project to inform the research design, analysis, and publication of results. We believe that board members can help to address several of the raised ethical problems, e.g., shaping the research questions to avoid feeding into existing biases or overexposing certain groups and highlighting potentially harmful interpretations and uses of our results.
Hovy and Spruit (2016) discuss issues that can arise when constructing datasets from social media and conducting analyses or developing predictive models based on these data, which we review here in relation to our project: Demographic bias in sampling the data can lead to exclusion of minority groups, resulting in overgeneralisation of models based on these data.
Answer: Yes, it can.