Ethics approval

The current study was approved by the Deakin University Human Ethics Advisory Group—Health (Project number: HEAG-H 140_2019). In line with HEAG-H advice, it was not possible to directly quote individual posts from publicly available datasets, thus data are presented in aggregated form only. Data were collected and used in accordance with Reddit’s Terms and Conditions.

Data analysis

Fig 1 outlines our processing and data analysis pipeline. Broadly, this comprised (a) data extraction, (b) data cleaning, (c) LDA topic modeling, and (d) qualitative synthesis. Of relevance, our approach to data cleaning and topic modeling is based on previously published work [42]. R software v3.6.1 [43] was used for all processing and analysis.

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Fig 1. Data processing and analysis pipeline.

https://doi.org/10.1371/journal.pone.0262529.g001

Data extraction

We scraped publicly available data using ‘RedditExtractoR’ v2.1.5 [44] from two forums focused on experiences of mothers and fathers on Reddit (i.e., r/Daddit; r/Mommit). Data scraping was conducted on 11 February 2020 and we retained those posts that were the first of a particular thread (known as the ‘original post’) and that contained textual data in the body of the post, e.g., did not comprise just an image. Responses to the original posts were not included in analyses as this study aimed to only identify common parenting situations rather than describe the conversations and language used surrounding these situations. Due to limits in how data can be scraped from Reddit, only the first 10 pages of original posts from these forums could be scraped at any one time. Thus, we extracted 340 (r/Daddit) and 578 (r/Mommit) original posts that spanned an approximate 4–6 week period prior to our date of data scraping (r/Daddit earliest original post extraction date was 13 Jan 2020; r/Mommit earliest original post extraction date was 12 Dec 2019).

Data preparation

The corpus of data were prepared for analysis using the ‘tm’ v0.7–6 [45] package. This process involved removing all non-alphabetical characters, punctuation, and blank spaces; converting all text to lowercase; removing all non-words; and removing “stop words” (e.g., “the”, “is”, “at”, “which”, “on”). We also manually ‘stemmed’ the corpus by reviewing the entire list of unique words and ensuring words with different suffixes (e.g., “happy”, “happiness”, “happiest”) were coded as the same word (e.g., “happy”). During this review we also corrected obvious misspellings and international differences in spelling.

LDA topic modelling

Common topics within the corpus were identified using latent Dirichlet allocation (LDA) [33], which is a common method used to identify themes in collections of scholarly textual data [32–35], as well as large sets of documents or texts in research or clinical contexts [46]. An outline of the statistical methodology of LDA is presented in detail elsewhere [33, 47], but briefly described here. LDA is a Bayesian probabilistic modelling method that aims to identify the unknown number of latent topics that are assumed to underlie a body of text [33]. LDA draws from a Dirichlet distribution to generate distributions of probabilities that describe how (1) words (i.e., word-topic-probabilities) and (2) documents (i.e., document-topic-probabilities) are related to the latent topics underlying the dataset. Specifically, word-topic-probabilities are estimates of the probability a word is generated from a specific topic, whilst document-topic-probabilities are estimates of the probability that a topic has been generated in a specific document [48]. Inspection of the highest word-topic and document-topic probabilities for each topic can help characterize the theme of each latent topic. In the current study, we used LDA to find common topics within the corpus of original posts from r/Daddit and r/Mommit, and from these we aimed to identify parenting situations.

To conduct the LDA, we converted the corpus to a document-term-matrix, comprising rows representing each original post (i.e., document = original post) and columns representing each word in the corpus (i.e., term = word). Each cell in the document-term-matrix contains the frequency of times a specific word (defined by the column) occurred in a specific post (defined by the row). From this document-term-matrix, the entire corpus was represented, including patterns of words that commonly occur together within the same post.

The LDA was conducted using the ‘topicmodels’ v0.2–8 package [49]. As per Kosinski, Wang [50], LDA hyperparameters were set to delta = 0.1 and alpha = 50/k (where k is the number of LDA topics being estimated). To identify the number of LDA topics that best fit the patterns within the corpus, we first estimated a 2-topic model and then sequentially increased the number of LDA topics being modelled until we estimated a 50-topic model. We then selected the best fitting model based on the perplexity value for each model; a common method for evaluating model fit in LDA models [33, 51], where models with lower perplexity are considered to fit the data better than models with higher perplexity. Our estimate of perplexity for each model was derived as an average of the perplexity across a five-fold cross validation process. Within this process, the corpus of original posts was randomly split into five portions with a model generated (i.e., trained) on four of the portions, and then validated on the fifth portion. This process was repeated until each portion had been used for validation once. We also used the ‘LDAvis’ v0.3.2 package [52] to obtain the percentage of tokens (i.e. words) contributing to a specific topic.

Qualitative analysis and interpretation

As mentioned previously, to interpret the topics derived from the optimal model, we relied on two metrics that form the basis of interpretation for LDA models. The first is a matrix of values that quantify the probability that each word on the corpus would be generated from a specific topic (i.e., β matrix; higher β = the word is more likely to occur in the topic). The second is a matrix of values that quantifies the proportion of words in an original post estimated to be generated by a specific topic (i.e., γ matrix; higher γ = the original post is more aligned with topic). Specifically, we focused interpretation on the 10 most relevant posts per topic according to γ values. We completed a qualitative synthesis of the topics and original posts via a manual text analysis to interpret each topic and characterize specific parenting situations described in original posts within each topic. This procedure involved classifying each post according to whether they met a pre-specified definition of a ‘parenting situation’, defined as follows: (1) the post referenced a scenario involving a child aged 0–18 years; and (2) the scenario involved a parent and their child; and (3) referenced a potential or actual difficulty or issue, for which the parent was giving or seeking advice in how to manage or improve. Posts were also only included if one or more of the 10 topic words was used in a way that conveyed meaning relevant to the central themes of a given post and topic, rather than being peripheral.

LDA results

Fig 2 presents the perplexity values for each of the topic model scenarios, modeling 2–50 LDA topics, based on the number of topics retained in our LDA. Dots represent perplexity scores for each of the 5-fold cross-validation models for each topic. The blue line represents the average perplexity scores across the 5-fold cross validation. The lowest average perplexity was for a model with 31 topics (mean perplexity = 917.64). This 31-topic model was found to be the best performing LDA model as it had the lowest average perplexity of all the models (see S1 Table). The β values for the 10 most probable words associated with each of the 31 LDA topics, and the range of γ values for the top 10 posts contributing to each of the 31 topics, are presented in S1 Fig and S2 Table, respectively. The average probability across all documents and the average and range of probabilities for the top 10 documents for each topic is presented in S3 Table. On inspection of the top 10 original posts related to each topic (i.e., with the highest γ values), 24 of the 31 LDA topics contained posts that met the inclusion criteria in terms of being consistent with our pre-specified definition of a parenting situation, and meaningfully reflecting the topic words (i.e., with the highest β values).

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Fig 2. Perplexity scores as a function of number of topics estimated.

https://doi.org/10.1371/journal.pone.0262529.g002

Parenting situations

Table 1 summarizes the LDA topic words, themes, and description of parenting situations for the 24 LDA topics that were included in the final analysis. The percentage of tokens contributing to each topic was relatively balanced, ranging from 2.6% (topic 31) to 4.1% (topic 2). Just eight of the 24 LDA topics included posts where parents were giving advice, either recounting their own recent experiences to illustrate a specific successful parenting moment that they hoped others might learn from (e.g., just found a good strategy for clearing their sick child’s congestion), or some parents posted with more generic advice (i.e., not specific to a recent parenting experience, but based on their accumulated parenting experience), such as providing advice on strategies for specific situations (e.g., toilet training advice). The remainder of the topics involved posts where the parent was seeking advice from other parents on the Reddit parenting forums, most commonly in the format of a parent describing a specific parenting situation and asking others for advice on how they might handle it.

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Table 1. Summary of topic words, themes, and situation descriptions related to parenting situations described in Reddit parenting posts.

https://doi.org/10.1371/journal.pone.0262529.t001

Based on the manual text analysis of posts within the LDA topics, we identified 45 unique but broadly defined parenting situations. The most prevalent themes from LDA topics meeting our inclusion criteria involved parents managing parenting situations related to child sleep, sickness, toilet training, child food refusal or fussiness, children whining when they didn’t get what they wanted, gender stereotyping, managing multiple children/siblings, and managing children who are showing a regression in their behavior in relation to age. There were two very common higher-order themes, each identified in more than a third of the parenting situations, relating to (1) managing basic childcare situations, i.e., related to eating, sleeping, routines, sickness, toilet training; and (2) how to respond to child negative emotions and/or difficult behavior. Some topics were generated from parenting situations based on just one post e.g., Topic 31’s sole post was seeking advice on burping a baby. Other topics were generated from parenting situations based on a number of posts e.g., Topic 2 had multiple posts on child sleeping habits.

Parent and child characteristics

Table 2 summarizes the child ages and subreddit source for each of the included topic themes. The majority of the included posts (80%) referred to a child or children of infant or toddler age. Posts about children 4 years and older were focused on child use of technology, supporting children in gender non-stereotypical activities (e.g., sports, make-up), managing sibling conflict or the needs of multiple children at home, and using bibliotherapy to talk to children about change or potentially upsetting subjects. In relation to the source of posts, one third of the included posts were from the r/Daddit forum, while the remainder were from r/Mommit. The majority of the LDA topics included posts from both sources, meaning that most of the identified parenting situations were discussed by mothers and fathers online. The two most common higher-order themes (i.e., managing basic childcare situations and responding to child negative emotions and/or difficult behavior) were also reflected in both r/Mommit and r/Daddit posts. The identity (e.g., gender) of the person writing each post was not known, and is therefore not able to be described.

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Table 2. Child ages and subreddit source reflected in included topic themes.

https://doi.org/10.1371/journal.pone.0262529.t002

Whilst we focused our interpretation on topics that met our inclusion criteria, interested readers can find a summary of themes for posts not meeting the inclusion criteria for the current study in S4 Table. In summary, parents posted on parenting subreddits for a large number of reasons, including medical issues not related to being a parent; managing relationships with family members other than their children, such as the relationship with their partner; and managing a range of situations related to being a parent, but not specific to a parent-child interaction, including moving house, seeking advice around their own feelings and wellbeing, navigating special occasions and holidays, their child’s school, and managing issues related to pets and children.

Principal results

The objective of the current study was to support the necessary first step in developing parenting interventions that are tailored to parents’ context, by providing a detailed understanding of the common parenting situations and issues that parents face day-to-day. In this study, we sought to identify the most common day-to-day parenting situations discussed online on the r/Daddit and r/Mommit subreddit forums to inform meaningful tailoring in parenting interventions. Using the innovative and increasingly popular machine learning approach, latent Dirichlet allocation (LDA) [33], we were able to exploit the incredibly rich source of person-centered parenting data available online, in order to identify parenting situations that are most applicable to parents day-to-day. The LDA topic modeling extracted 31 LDA topics, of which 24 met our inclusion criteria in terms of being related to a difficult parenting situation involving their child. From these, we identified 45 unique but broadly defined parenting situations involving a parenting issue or difficult interaction between a parent and their child aged 0–18 years.

Our study is the first we are aware of to systematically describe the most topical and common parenting situations that parents seek advice and support around. Over two-thirds of the LDA topics meeting our inclusion criteria for analysis related to two primary themes. First–managing basic childcare situations, i.e., related to eating, sleeping, routines, sickness, toilet training; and second–related to advice on how to respond to child negative emotions and/or difficult behavior. Although there is little in the way of published evidence documenting the specific parenting examples used in parenting interventions, it is the experience of the authors that these examples tend to relate to situations involving the second theme, i.e., related to managing child negative emotions and difficult behavior. Our results suggest that many parents are also very interested in how to manage children in day-to-day childcare tasks. The parent-child relationship is equally formed around management of these daily rhythms, as it is in the management of difficult child behavior or emotions. Thus, it seems appropriate that future parenting interventions could include content focused on parent-child interactions across the breadth of parenting situations described in our results.

The inclusion of posts from r/Daddit and r/Mommit meant that our results reflected perspectives from both mothers and fathers. Despite clear evidence that fathers are central in influencing child developmental outcomes [53, 54], including child mental health outcomes [19, 55], fathers are chronically underrepresented in parenting research and parenting interventions (<20% of attendees) [56]. A systematic review of fathers’ representation in observational parenting studies identified that just 10% of 667 parenting studies included results separately for fathers; 1% were focused solely on fathers compared to 36% focused solely on mothers [57]. Our study findings address this gap by describing topics discussed by mothers and fathers. Although we identified some differences in LDA topics discussed in each of the forums, on the whole, there was a high level of consistency in the themes raised in r/Daddit and r/Mommit. However, our findings suggest that parents raise different questions depending on the age of their child, suggesting that parenting interventions should tailor intervention content to be age-specific. Further, the vast majority of included posts (80%) related to infant or toddler aged children. These findings suggest that early childhood may be an important time for intervention when parents are particularly open to receiving support and advice.

We note that just 24 of the 31 LDA topics identified met our inclusion criteria for analysis as a parenting situation. The remaining posts traversed a wide range of themes, including managing family situations and relationships in the extended family context, parents’ own health concerns, and household-related issues. Parents increasingly use online tools in a multitude of ways to assist with parenting, including connecting through social media, assisting with managing family life, and providing multiple sources of online information [29]. Our data reflect that parents use Reddit to access support from other parents for a wide range of reasons. Although some of the LDA topics identified in the current study were not deemed to be immediately relevant in terms of tailoring parenting interventions, they may be useful for understanding how parents access support online.

Applications for tailoring parenting interventions

E-interventions, delivered online or via smartphone apps, offer potential for overcoming cost, time, and geographic barriers faced by traditional interventions [58]. However, results to-date have been disappointing. A systematic review showed that current technology-based parenting interventions are still under-servicing key groups, such as fathers, non-urban parents, and parents from a low socio-economic background [59]. For the most part, technology-based parenting interventions have not changed the format or content from traditional face-to-face, module-based approaches [58]. Tailoring in technology-based parenting interventions has been minimal to-date, despite evidence that that engagement is strongest when interventions are flexibly tailored to be maximally relevant to individual parents [58, 60]. Findings from the current study provide scope to address these limitations. We identify 45 of the most commonly discussed parenting situations on the parenting-specific forums of Reddit. Our results provide a detailed picture of the common scenarios that mothers and fathers face day-to-day, that could be applied to tailor resources in population-based parenting interventions.

Strengths and limitations

The use of LDA topic modeling and manual text analysis was both a strength and potential limitation of the current study. LDA offers a powerful data-driven method for analyzing large datasets, and may discern patterns in qualitative data that becomes infeasible to analyze with traditional qualitative coding approaches as data quantity increases. However, LDA lacks specific recommendations around best practice approaches in particular settings. For example, there is no single method for determining the most appropriate number of LDA topics to model within the data [32]. Further, decisions made in the data preparation steps to clean the data prior to running the LDA modeling have potential to introduce researcher bias and impact the results.

Our findings may also be limited by the scope offered in the Reddit parenting forums. For example, while widely used, not all parents use Reddit. Further, for Reddit users, some parents may be more likely to comment on others’ posts, rather than create an original post themselves. In both cases, the experiences of these parents will not be reflected in our results. There may also be bias in the subjects that parents raise for discussion on Reddit, for example, based on parents’ prior interpretation of what is acceptable within particular forums. In addition, some topics may be less likely to be identified by the LDA process, such as those related to sensitive or less common situations, or where a range of different words are used to refer to the same meaning, thus making it less likely to be identified as a topic by the LDA modelling. Further research efforts to augment publicly available posts with data directly obtained from Reddit users about their demographics and other parenting-related constructs may provide added context with which to appraise the potential generalizability of results obtained from online forum conversations.

Our results reflect data collected from the Reddit platform in 2020, but are likely to be generalizable to other online forums used by parents, such as Facebook and Instagram. There is emerging evidence investigating the way in which parents use other social media sites, including Facebook parenting groups [61, 62]. However, Reddit is uniquely amenable to topic modelling, given that all posts are publicly available, which is not the case with Facebook and Instagram. We expect that the way in which parents discuss parenting issues would be relatively consistent across platforms, but there is currently no evidence to assess this claim. This could be tested by future research investigating whether there are systematic differences between parent users of different platforms, such as demographic characteristics (e.g., parent user age, gender, country of residence, spoken language).

Conclusions

Our results support the use of LDA text mining for the purpose of understanding broad themes discussed in online parenting forums. We identified 45 unique parenting situations describing a wide range of parenting contexts, but most commonly related to basic childcare situations, i.e., related to eating, sleeping, routines, sickness, toilet training; or related to advice about how to respond to child negative emotions and/or difficult behavior. The current study was novel in analyzing parents’ own words to understand the most common day-to-day parenting issues experienced by parents. Internet forums represent a rich source of potentially unbiased data for researchers, offering a method for observing and analyzing naturally-occurring adult conversations online. These findings offer potential to tailor parenting interventions in a meaningful way, creating opportunities to develop content and resources that are directly relevant to parents’ lived experiences.