World Happiness Report 2023 156 over time as they adapt to the requirements of people and their surroundings.123 It is possible to document semantic drift using machine learning techniques acting over the span of 5-10 years.124 Because of semantic drift, machine learning models are not permanently stable and thus may require updating (retraining or “finetuning”) every decade as culture and language use evolve. Limitations: Changes in the Twitter platform An uncertain future of Twitter under Musk. The accessibility of social media data may change across platforms. For example, after buying and taking over Twitter at the end of 2022, Elon Musk is changing how Twitter operates. Future access to Twitter interfaces (APIs) presents the biggest risk to Twitter for research, as these may only become accessible subject to high fees, with pricing for academic use currently uncertain. There are also potentially unknown changes in the sample composition of Twitter post-November 2022, as users may be leaving Twitter in protest (and entering it in accordance with perceived political preference). In addition, changes in user interface features (e.g., future mandatory verification) may change the type of conversations taking place and sample composition. Different account/post status levels (paid, verified, unverified) may differentiate the reach and impact of tweets, which will have to be considered; thus, temporal models may likely have to account for sample/ platform changes. A history of undocumented platform changes. This is a new twist on prior observations that the nature of the random sample and language composition of Twitter has changed discontinuously in ways that Twitter has historically not documented and only careful analysis could reveal.125 For example, it has been shown that changes in Twitter’s processing of tweets have resulted in corrupted time series of language frequencies (i.e., word frequencies show abrupt changes not reflecting actual changes in language use but merely changes in processing – such as different applications of language filters in the background).126 These corrupted time series are not documented by Twitter and may skew research. To some extent, such inconsistencies can be addressed by identifying and removing time series of particular words, but also through the more careful initial aggregation of language into users. Methods relying on the random aggregation of tweets may be particularly exposed to these inconsistencies, while the use of person-level and cohort designs (Gen 2 and 3) that rely on well-characterized samples of specific users may likely prove to be more robust. Future directions: Beyond social media and across cultures Data beyond social media. A common concern for well-being assessments derived from social media language analyses is that people may fall silent on social media or migrate to other social media platforms. It is hard to imagine that social media usage will disappear, although there will be challenges with gathering data while preserving privacy. In addition, work suggests that other forms of communication may also be used. For example, individuals’ text messages can be used to assess both self-reported depression127 and suicide risk128; and online discussion forums at a newspaper can be used to assess mood.129 The limiting factor for these analyses is often how much data is easily accessible, public-by-default social media platforms such as Twitter and Reddit generate data that is considered in the public domain. This is particularly easy to collect at scale without consenting individual subjects. Measurement beyond English. Beyond these difficulties within the same language, more research is needed in cross-cultural and cross- language comparisons. Most research on social media and well-being is carried out on single-language data, predominantly in English. A recent meta-analysis identified 45 studies using social media to assess well-being, with 42 studying a single language, with English being the most common (n = 30);130 To improve the potential of comparisons across languages, more research is needed to understand how this may be done. One potential breakthrough in this domain may be provided by the recent evolution of large multi-language models,131 which provide shared representations in multiple common languages and, in principle, may allow for the simultaneous
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