JMIR Publications

ABSTRACT

Background:

Intimate partner violence (IPV) is an underreported public health crisis primarily affecting women associated with severe health conditions and a high rate of homicide. Due to the COVID-19 pandemic, more women with IPV experiences visited online health communities (OHCs) to seek help because of anonymity. However, little is known regarding whether their help requests were answered and whether information provided was delivered in an appropriate manner. To understand the help-seeking information sought and given in OHCs, extracting postings and linguistic features could be helpful to develop automated models to improve future help-seeking experiences.

Objective:

The objective of this study is to examine the types and patterns (i.e., communication styles) of the advice offered by OHCs members and whether the information received from women matched their expressed needs in their initial postings.

Methods:

We examined data from Reddit using data from subreddit community r/domestic violence posts from November 14, 2020, through November 14, 2021, during COVID-19. We included posts from women 18 years or older who self-identified/described experiencing IPV and requested advice or help in this subreddit community. Posts from non-abused women, women under 18, non-English posts, good news announcements, gratitude posts without any advice-seeking, and posts related to advertisements were excluded. We developed a codebook and annotated the postings in an iterative fashion. Original posts were also quantified using the Linguistic Inquiry Word Count (LIWC) to categorize linguistic and postings features. Postings were then classified into two categories (i.e., matched needs and unmatched needs) according to the types of help sought and received in OHCs to capture the help-seeking result. Non-parametric statistical analysis (i.e., t-test or Mann-Whitney U) were used to compare the linguistic/postings features between matched and unmatched needs.

Results:

A total of (N=250) postings were included, 80% (n=200) of posting response comments matched with the type of help requested in initial postings with legal advice and IPV knowledge achieving the highest matching rate. 17 linguistic or postings features were found to be significantly different between the two groups (i.e., matched help and unmatched help). Negative title sentiment and linguistic features in postings containing health and wellness wordings were associated with unmatched needs postings, while the other 14 features were associated with postings with matched needs.

Conclusions:

OHCs can extract the linguistic and posting features to understand the help-seeking result among women with IPV experiences. Features identified in this corpus reflected the differences found between the two groups. This is the first study that leveraged LIWC to shed light on generating predictive features from unstructured text in OHCs, which could guide future algorithms development to detect help-seeking results within OHCs effectively. Clinical Trial: N/A