Dirty laundry: The nature and substance of seeking relationship help from strangers online

Interpersonal relationships are vital to our well-being. In recent years, it has become increasingly common to seek relationship help through anonymous online platforms. Accordingly, we conducted a large-scale analysis of real-world relationship help-seeking to create a descriptive overview of the nature and substance of online relationship help-seeking. By analyzing the demographic characteristics and language of relationship help-seekers on Reddit (N = 184,631), we establish the first-ever big data analysis of relationship help-seeking and relationship problems in situ among the general population. Our analyses highlight real-world relationship struggles found in the general population, extending beyond past work that is typically limited to counseling/intervention settings. We find that relationship problem estimates from our sample are closer to those found in the general population, providing a more generalized insight into the distribution and prevalence of relationship problems as compared with past work. Further, we find several meaningful associations between relationship help-seeking behavior, gender, and attachment. Notably, numerous gender differences in help-seeking and romantic attachment emerged. Our findings suggest that, contrary to more traditional contexts, men are more likely to seek help with their relationships online, are more expressive of their emotions (e.g., discussing the topic of “heartache”), and show language patterns generally consistent with more secure attachment. Our analyses highlight pathways for further exploration, providing even deeper insights into the timing, lifecycle, and moderating factors that influence who, what, why, and how people seek help for their interpersonal relationships.


Supplementary Materials A: An Analysis of Gender versus Submission Flair
We conducted additional, descriptive analyses to compare the frequencies of the various submission categories assigned to r/relationships submissions between male and female users. These categories are also known as "flairs" in the context of Reddit and, within r/relationships, are used to label each user submission as pertinent to a specific topic. For example, a user submission that has been assigned an "infidelity" flair signals that the submission content is primarily about infidelity within one's relationship. Figure S1 shows how there were generally few/small gender differences in the general relationship topics discussed -among both men and women, the general "relationships" flair was by far the most frequently assigned to submissions, this was followed by the "break-ups" and "dating" flairs, which were assigned at fairly equal rates, and finally, the "infidelity" flair was the least frequently assigned to submissions. The largest gender differences were in relation to the dating and break-ups categories, with men seeking relationship help for dating and break-ups more than women.

Relative Frequencies of LIWC Scores by Gender, Visualized
In order to get a clearer visual sense of gender differences in language use from a LIWC perspective, we present below a figure of the relative percentages of each language measure by gender. Figure S2 may be interpreted as another way of demonstrating that, while differences do exist between men and women in our data, most differences are relatively small. Of the greatest note, men used more prepositions (Cohen's d = .17), whereas women used higher rates of language consistent with depression and emotional upheavals; namely, negative emotion words (broadly defined; d = .16), anxiety words specifically (d = .15), and first-person singular pronouns (d = .14).

Automated Detection of Theme Presence/Absence
Simply described, our goal was to only classify those submissions as containing each theme when there was clear evidence for a significant appearance of clusters of words related that theme were contained within the submission. For example, it would be inappropriate to classify a submission as containing the housework theme simply because the word "clean" appears within the text -the author may be referring to a "clean breakup" or a "clean slate" or some other sense of the word. However, if a submission contains several housework-related words, such as clean, chore, vacuum, dishes, and so on, we can be more confident that this submission is on-topic for this theme. Statistically, then, our goal was to establish a "noise floor" -a numerical threshold that would differentiate which texts contained an errant word or two that may or may not be related to a particular MEM theme versus texts that contained a sufficient number of theme-relevant words to be classified as containing that theme.
Automatic theme recognition was performed by quantifying the relative frequency of theme-related words in each submission, then comparing these values against theme-specific noise floors identified using the At Most One Change (AMOC) change point detection algorithm (see Killick & Eckley, 2014). Any submission scoring above each floor for any given theme was classified as containing that particular theme. Figure S3 provides an example of how the detection process with AMOC operates for the housework theme. MEM themes have a theoretical boundary from -100 to +100, with most texts scoring in the region around 0 (indicating an absence of a given theme). Via the AMOC algorithm, we establish 2 changepoints: the point at which any given text trends upwards toward zero, and the point at which any given text trends upwards away from zero; it is this latter changepoint that is of interest in the current context for each theme. We note here that this method -like all statistical methods for changepoint detection -is not perfect and, in some cases, may skew towards liberal or conservative inclusion points relative to what a human coder may judge. Rather, we emphasize that this method was used to heuristically identify texts that were likely to contain each MEM-derived theme. Figure S4 illustrates the distribution of MEM theme presence across our sample, with numbers along the X-axis reflecting the number of themes detected within any given submission.  Table S1.

Figure S3
Use of the AMOC Algorithm to Establish a "Noise Floor" Above which Any Post would be Classified as Containing the Housework Theme

Figure S4
Distribution of the number of themes detected across r/relationships submissions, with a median of 3 found in our sample.

Supplementary Materials D: Gender Differences in MEM Theme Use
In the main body of the manuscript, we presented all gender difference analyses in MEM themes in the form of boxplots to provide an easy-to-navigate overview of the findings and to illustrate the general similarities between men and women. Here, we provide a more thorough account of the statistical analyses in the form of Table S2, below.
Importantly, note that while the effect sizes may be considered "small" using traditional interpretation guidelines, there today exists a common consensus that the size of an effect is not a meaningful indicator of its relative "importance" for several reasons. First, traditional social science research has relied on small sample sizes, resulting in 1) the need for an effect to be "large" to be detectable, 2) an inaccurate fixation on large effect sizes as markers of importance, and 3) gross over-estimates of common effect sizes, leading in large part to the current "replication crisis" (see, e.g., Anderson & Maxwell, 2017;Button et al., 2013). Rather, it is now fairly well-understood that small effects can be particularly important when they 1) occur in non-trivial contexts (such as in the context of real-world help-seeking and relationship problems, rather than an artificial lab study), 2) challenge existing theory and assumptions (as many of our current effects do), and/or 3) can have large cumulative consequences. For additional reading, we recommend Cortina and Landis (2009) and Matz et al. (2017). Note. Means refer to percentages of MEM themes discussed within each r/relationships submission. CI = confidence interval.