Introduction

Trigger warnings and content warnings are “a statement at the start of a piece of writing, video, etc. alerting the reader or viewer to the fact that it contains potentially distressing material–often used to introduce a description of such content” (p.602) [1]. This dual use–both warning about, and characterising the content of, material–may account for the terms ‘trigger warning’ and ‘content warning’ being used interchangeably across the diverse academic disciplines which research their use. Some but not all studies locate trigger warnings as a particular sub-type of content warnings which are focussed specifically on the needs of people with experience of trauma or post-traumatic stress disorder (PTSD). In addition, the use of ‘trigger’ has been disputed because what constitutes a ‘trigger’ in relationship to PTSD is highly individual and unpredictable [2]. In this paper we therefore use the general term ‘content warning’.

The use of content warnings is longstanding and widespread across multiple sectors, e.g. health, education, media, arts and literature. The concept of ‘triggering’ describes the re-experiencing of unpleasant PTSD symptoms such as intrusive thoughts being evoked by exposure to materials which spark traumatic memories. Hence, content warnings have a long presence in psychiatric literature [3]. Similarly, most countries use classification systems for film and television media, such as Australia [4] and Singapore [5]. For example, a film rating classification system has been used in the United Kingdom (UK) since 1912. In education, use of content warnings is widespread [6], e.g. a 2016 survey of professors in the United States of America (USA) found that 428 (51%) of the 841 respondents reported some use of content warnings in their classes [7]. Content warnings have also been advocated and used in other sectors, including books [8], comics [9] and museums [10]. Multinational frameworks have been established, such as the Pan European Game Information (PEGI) categories of age labels [3, 7, 12, 16, 18] and content descriptors (Bad language, Discrimination, Drugs, Fear, Gambling, Sex, Violence, In-game purchases) for computer games.

There is no inter-sectoral consensus or widely used typology of content warnings. Incompatible frameworks have been developed, driven by the perceived need for different uses and in different legal and geographical jurisdictions. One reason for differences in content warning is the un-coordinated development of frameworks across different sectors. Each sector contains different assumptions, so for example in the arts sector provocation caused by displayed materials is valued and intended [11], with content warnings primarily giving information so individuals can make their own choices about exposure. By contrast, in the health sector the biomedical ethical imperative of non-maleficence [12] places more responsibility on the health professional to actively reduce the likelihood of triggering trauma responses in people with experience of PTSD. Hence, the content warnings in arts spaces tend to be more focussed on information, whereas those in a health context contain less information to avoid the content warning itself being triggering.

The development of shared inter-sectoral practices such as consensus on a content warning typology is made more difficult because of the contested evidence base. Broadly, some researchers argue that content warnings are a form of over-protection which inadvertently hinder the development of resilience [13], whereas others argue that content warning allows both avoidance of un-wanted exposure experiences and emotional preparation to reduce negative reaction to content [14]. More recent studies have experimentally investigated the impact of content warnings, especially in educational settings. A randomised study found participants with no trauma history (n = 133) who received warnings before reading passages with disturbing content reported more anxiety than those not receiving (n = 137) warnings, suggesting warnings can undermine emotional resilience [15]. The same authors replicated this finding with a college student sample (n = 462) [16], and also showed in a randomised study of trauma survivors (n = 451) that content warnings inadvertently reinforce the centrality of trauma experiences to identity [17]. A 2019 meta-analysis confirmed this finding that content warnings are associated with increased anxiety and negative mood [1]. Meta-analyses of a series of studies involving students and internet volunteers, with and without a trauma history, found mainly neutral or slightly negative impact of content warnings, leading the authors to conclude that such warnings are neither meaningfully helpful or harmful [18].

Given the emerging empirical evidence base, and the publication of balanced and authoritative overviews [19], why is there difficulty in reaching consensus? Despite early suggestions in the 2000s of a new interdisciplinary area emerging called warning research [20], and signs of social media conventions developing [21, 22], much of the public discussion about content warnings has been heated. The use of emotive language such as the ‘trigger warning war’ [23] is perhaps related to wider cultural debates and potentially clashing priorities, for example free speech and censorship approaches versus trauma-informed and rights-informed approaches. The issue is international, and passionate articles have been written by authors in Australia [24], Ireland [25], the UK [26] and the USA [27]. For example, some have argued that, whatever the experimental evidence, content warnings are a vital approach to increasing inclusivity on campuses because they can support engagement by people who would otherwise avoid material due to past experiences [28]. Others cite evidence that content warnings can be useful in specific educational contexts, such as victimology courses [29].

An inter-sectoral typology of content warnings would advance the field, in three ways. First, by allowing the comparability of findings from across different studies to be maximised. At present there is no recommended typology, and so empirical studies use different content warnings for the same content. Identifying an agreed description for a warning and locating it in a broader and coherent typology will support more fine-grained investigation about the impact of specific types of warning. In addition, the typology will be of interest to a range of fields, including arts, media studies, medicine, mental health, and psychology. Second, the limits of content warnings would be helpful to establish, to understand what is in scope. For example, are ‘Contains nuts’, ‘Contains flashing imagery’ and the perhaps ironic ‘Depicts killer robots’ all appropriately understood as content warnings? Finally, identifying the typology, sector, presentation format and target population will allow more specific future research into the positive and negative impacts of content warning when used in a specific sector with a specific target population.

The aims of this review are (1) to develop a typology of content warnings, and (2) to identify the contexts in which content warnings are used, comprising the sector (e.g. education, health), format (e.g. film, music) and target audience.

Method

The protocol of this systematic review was developed in accordance with PRISMA guidelines and was registered on PROSPERO (International Prospective Register of Systematic Reviews) on 9 July 2020 (reference CRD42020197687). The study was conducted as part of the Narrative Experiences Online (NEON) study (http://www.researchintorecovery.com/neon), which aims to evaluate the impact of recorded recovery narratives [30] when used as a mental health intervention [31]. In developing the intervention [32], we needed to decide whether to use content warnings when delivering recorded recovery narratives to participants in randomised controlled trials for people with experience of psychosis (NEON Trial: ISRCTN11152837), for people with experience of non-psychosis mental health problems (NEON-O Trial: ISRCTN63197153), and for informal mental health carers (NEON-C trial: ISRCTN76355273) [33]. The typology developed in this review is therefore called the NEON content warning typology.

Results

The search identified 6,254 documents, from which 136 were included. The flow diagram is shown in Fig 1.

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Fig 1. Flow diagram for included documents.

https://doi.org/10.1371/journal.pone.0266722.g001

The data abstraction table for all included documents is shown in S2 Appendix. The 136 included documents comprised webpages (n = 43), academic journals (n = 38), newspaper/magazines (n = 36), technical reports (n = 12), books (n = 4), conference abstracts (n = 2), and a thesis (n = 1). Of these, 38 (28%) documents were peer-reviewed studies and 98 (72%) were non-peer reviewed. The included documents came from 32 countries covering North America (n = 74), Europe (n = 22), international which covered multiple countries (n = 15), Asia (n = 14), Australasia (n = 4), Africa (n = 3), both Europe and USA (n = 1), and South America (n = 1). The majority of documents came from the USA (n = 69) (51%).

Only 18 (10%) documents reported empirical studies, comprising observational (n = 9), experimental (n = 6), qualitative (n = 2), and mixed methods (n = 1) designs. As a result of this small proportion, it was not appropriate to conduct a sensitivity analysis comparing higher to lower quality studies. Therefore, the MMAT ratings, although retained in the data abstraction table, were not used in the analysis.

Across the 136 documents, 330 warning lists were identified containing a total of 2,209 warnings (including duplicates). The documents comprised 52 (16%) defining a list of warnings and 278 (84%) outlining specific warnings for use in a specific context, e.g. classroom. Across all lists, 134 (41%) were labelled as trigger warnings and 196 (59%) as content warnings.

Objective 1: NEON content warning typology

The 2,209 warnings were synthesised into 14 categories of content warning, as shown in Table 1.

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Table 1. NEON content warning typology.

https://doi.org/10.1371/journal.pone.0266722.t001

The complete NEON content warning typology, including definition, number of documents using the category, and example text for all sub-categories, is shown in S3 Appendix.

Objective 2: Contexts for content warning use

Ten sectors using content warnings were identified in the 136 documents: education (n = 53), audio-visual industries (n = 34), games and apps (n = 18), media studies (n = 4), social sciences (n = 3), comic books (n = 3), social media (n = 3), music (n = 2), mental health (n = 2), science and technology (n = 1). Thirteen documents spanned multiple sectors. Compared to other sectors, Education had the most frequent mentions of warnings relating to Violence, Sex, Stigma, Risky behaviour, Mental health, Abuse, Crime, Socio-political, and Objects. Audio-visual industries were the highest users of warnings about Disturbing content, Language, Death, and Parental guidance. Games and apps were the sector most using warnings about Flashing lights.

A total of 15 presentation contexts for the use of content warnings were identified in the 330 lists: education materials (n = 95), film (n = 48), games (n = 43), websites (n = 32), television (n = 16), books (n = 15), social media (n = 9), verbally (n = 5), print media (n = 4), apps (n = 3), radio (n = 2), music (n = 2), research (n = 1), DVD/video (n = 1), policy documents (n = 1) and multiple contexts (e.g. classroom and online forums) (n = 59). Educational materials was the most frequently used presentation format for content warnings relating to Violence, Sex, Stigma, Mental health, Abuse, Socio-political, and Objects. Disturbing content, Language, and Risky behaviours content warning categories were most frequently used in Games. Film was the most used presentation format for Death, Parental guidance and Crime.

The target audience in the 330 warning lists comprised mainly the general public (n = 197) and students (n = 119), with other recipient groups identified including children and adolescents (n = 8), women (n = 3), parents (n = 2), and people with post-traumatic stress disorder (PTSD) (n = 1). Generally, the included papers did not explicitly state the target audience for the content warning, so the target audience was recorded as general public. Students were a highly cited target audience across all warning categories, and were more cited than the general public for the Mental health warning. For the remaining thirteen warning categories, the most cited target audience was the general public.

Discussion

This systematic review included documents from 32 countries and developed the NEON content warning typology comprising 14 categories of warning. The categories of Violence, Sex and Stigma were the most widely used warnings, but some warnings were more sector-specific, such as Parental Guidance in relation to films. Ten distinct sectors in which content warnings are used were identified, along with 15 distinct presentation formats. The target audience for content warning usage was often not explicitly stated.

Several principles informed the typology development. First, content warnings relating to media content and not product safety were included. For example, product labelling for food (e.g. Contains nuts) and cleaning products (e.g. Keep out of reach of children) were deemed out of scope in this review. The category of Flashing Lights was a boundary condition which was included due to its use in films, even though it is also used on consumer products such as toys.

Second, the need for accessible language informed label choices where possible, e.g. Anti-disability was chosen over Ableism. However, preference was given to retaining the meaning, e.g. Misogyny and Misandry were selected since labels such as Anti-female and Anti-male did not adequately capture the full meaning of gender-based prejudice. These terms are spreading due to increased use and the increasing rights literacy among younger people [36].

Finally, the NEON content warning typology is designed to be extendable in scope and depth. This may be needed for several reasons.

1. The decision to exclude warnings that perpetuate structural inequality as part of the synthesis led to the exclusion of a Same-sex relationship warning, in order to avoid replicating heteronormative assumptions and prejudice. Alternative or additional approaches to preventing the perpetuation of structural inequality could be considered.
2. The categories in the typology are not wholly distinct. For example, Anti-Semitic was positioned in the typology as a sub-category of Anti-religious due to its use in included documents, but could also be located as a form of Racism. Similarly, there is an inter-linkage between the Stigma and Sociopolitical categories, and the challenges of locating categories in either (e.g. Racism and Sexism in Stigma and Injustice in Sociopolitical) or both (e.g. Classism in Stigma and Class issues in Sociopolitical) reflect that these complex issues can be viewed both as individual and structural problems. Similarly, warnings about depictions of violence in relationships may draw from both the Violence and Abuse categories.
3. The typology was constrained to reflect the content of included documents. This meant that some categories would benefit from future disaggregation or elaboration. For example, Political issues incorporates both pro-capitalist and anti-capitalist content, so a future iteration may develop sub-categories to differentiate these two very different concerns. The Risky Behaviours category currently does not include any online behaviours, which may be a future extension. Other candidate extensions include a Pro-religious warning in the Stigma category and a Sexualising of children warning in the Abuse category.

Globally, the translation of content warnings is an important consideration. For example, content warnings in media and printed materials may vary between differing countries and resource settings due to specific cultural and contextual factors, mediated by the power held by organisations that specify the use of content warnings, such as film classification bodies. Examples might include different thresholds in relation to depictions of sexual behaviour, drug use and clothing, which can change over time. In addition, content warnings may not be used in some settings, highlighting the prominence of content warnings in particular contexts such as higher education. The translation and use of content warnings in other settings will need to be considered. Methodologies now exist to support the proportionate translation of the content warnings into other languages [37]. In relation to use, when presenting stories in different settings, as will be done in the NEON study, specific cultural or contextual content warnings may need to be included to meet the needs of recipients from different cultural backgrounds.

Overall, the typology is designed to be extended, as social norms evolve and as research into content warnings develops. The NEON content warning typology reflects current inter-sectoral research and practice, and so refinement within its overall structure is actively encouraged and recommended.

Implications

The study has three implications for future research. First, this review can support the emergence of a coherent and aggregable evidence base allowing the impact of content warnings to be more systematically investigated. The use of content warnings relates to other safety-driven initiatives. A recent systematic review investigating removal or blurring of self-harm online imagery found parallel issues of potential harm and positive impacts [40], reinforcing that research in content warnings and cognate areas is complex. There is a need to move beyond the current somewhat simplistic focus on whether content warnings help or harm towards a contextualised understanding of the mechanisms by which content warnings impact on recipients with different characteristics in different sectors and when used for different purposes. Future content warning studies should clearly describe the specific warnings used and the context including sector, presentation format and target audience. Using the NEON content warning typology and identified usage descriptors such as the names for the ten sectors will increase opportunities to integrate the currently diverse evidence base. A recommended citation terminology for use in studies using the NEON content warning typology would be identifying the specific warning(s) using the category number and name (e.g. ‘NEON 1.3 Weapons’) and specifying the sector, format and target audience involved in the research. This review found no consensus about the use of the term ‘trigger warning’ versus ‘content warning’, so to ensure relevant studies can be easily located in future reviews it is recommended that abstracts for relevant studies always include the term ‘content warning’. Overall, these recommendations will support the development of a more robust and integrated scientific evidence base, which can illuminate issues such as the optimal granularity of a warning (Violence? Motiveless killing?) and the mechanisms of impact on different groups in different contexts.

A second and related future focus could be on a more fine-grained perspective about differential impacts of content warnings on specific target audiences. Most included documents did not explicitly specify the audience, which is an important omission. For example, there is emerging evidence that post-traumatic growth in psychosis is more common than expected [41, 42], which may inform the use or not of content warnings with this clinical population. Similarly, greater clarity about the relationship between positive and negative impacts of warnings on specific audiences would allow personalisation of individually delivered content warnings in online apps, games, and web-browsing. For example, web-browser extensions have been developed which automatically detect potentially sensitive content warnings for specific topics, with the goal of creating safe internet spaces for users [43]. Whilst The NEON content warning typology presents a list of content warnings, it is limited to reflect the included documents. However, the typology could inform creators of such technologies about what content warnings exist and what topics and/or words need to be included and screened for. Similarly, for audiences, future web-based innovations may allow individuals to choose what specific content warnings to screen that meets their needs, and the typology can support individuals in deciding which specific topics/words to include or exclude. Further personalisation of content may also include recording recipient characteristics as part of a personal profile on a smartphone which would allow apps to be tailored to include content warnings tailored to individuals with particular characteristics, or to include content warnings only for some recipients based on their personal profile.

A third area of research is to investigate secular or time trends in the use of different types of warning. For example, in this study Stigma was one of the most widely used categories, which may reflect an increasing global focus on issues of rights and discrimination, and the links between being a member of a stigmatised group and experiencing trauma [44]. Future research might investigate whether the changing pattern of content warning use over time can be used as a barometer to capture global socio-political trends, such as the increasing recognition of the ongoing trauma effected by historical colonialism, racism, and trans-Atlantic slavery. Changing patterns of content warning use may in this way illuminate wider societal changes, giving a new source of data for social science research seeking to characterise the evolution of societal values and priorities.

Finally, the next stage in research may involve addressing the short-term nature of content warnings. The NEON content warning typology provides a framework for alerting to potential distressing and/or sensitive content, but in other environments such as academia, content warnings are provided alongside faculty and student service support. Evaluation will need to identify if any additional resources beyond the typology alone are needed to support recipients when content warnings are in different settings, and whether the typology is useful for recipients in deciding what content to engage with.

Overall, the NEON content warning typology is an empirically-defensible theoretical foundation for future content warnings research. It has been developed by analysts from diverse professional and academic perspectives, enhancing its intersectoral applicability. Using the typology will support investigation addressing the important and currently under-researched question of how different types of content warnings impact on different audiences in different sectors.

Supporting information

Acknowledgments

We thank Ms Emma Young (Nottinghamshire Healthcare NHS Foundation Trust) for support.