Related Work

[azpoliak]

• [x] Narrative Paths and Negotiation of Power in Birth Stories (CSCW 2018)

  Birth stories have become increasingly common on the internet, but they have received little attention as a computational dataset. These unsolicited, publicly posted stories provide rich descriptions of decisions, emotions, and relationships during a common but sometimes traumatic medical experience. These personal details can be illuminating for medical practitioners, and due to their shared structures, birth stories are also an ideal testing ground for narrative analysis techniques. We present an analysis of 2,847 birth stories from an online forum and demonstrate the utility of these stories for computational work. We discover clear sentiment, topic and persona-based patterns that both model the expected narrative event sequences of birth stories and highlight diverging pathways and exceptions to narrative norms. The authors’ motivation to publicly post these personal stories can be a way to regain power after a surveilled and disempowering experience, and we explore power relationships between the personas in the stories, showing that these dynamics can vary with the type of birth (e.g., medicated vs unmedicated). Finally, birth stories exist in a space that is both public and deeply personal. This liminality poses a challenge for analysis and presentation, and we discuss tradeoffs and ethical practices for this collection. WARNING: This paper includes detailed narratives of pregnancy and birth.

• [x] Fathers’ Worries During Pregnancy: A Qualitative Content Analysis of Reddit (Journal of Perinatal Education 2018)

  Qualitative investigations into the emotional needs of expectant fathers have been limited by difficulties with recruitment. This study aimed to unobtrusively obtain insights into fathers’ worries during pregnancy by analyzing the content of posts on the Internet forum Reddit. The majority of worries related to infant well-being (50.8%), particularly the potential for perinatal loss (23.0%). Concerns relating to partner well-being and the couple relationship were also common, comprising 17.0% of posts. Several posts related to individual factors, such as apprehension about the father role (16.3%). Finally, situational factors such as work–family conflict accounted for 15.9% of posts. These findings contribute to the growing literature on fathers’ experiences of pregnancy and can inform the development of father-inclusive perinatal education.

• [ ] Mothers’ Worries During Pregnancy: A Content Analysis of Reddit Posts(https://connect.springerpub.com/content/sgrjpe/26/4/208) (Journal of Perinatal Education 2020)

  Qualitative investigations into maternal worries during pregnancy are limited. The aim of this study was to identify the content of women’s pregnancy-related worries by completing a content analysis of posts on Reddit. A total of 217 posts by 196 unique users were analyzed. Most worries related to infant factors (32.6%), individual factors (27.1%), and antenatal care factors, such as medical procedures (25.2%). The remaining worries related to situational factors (10.9%) and the partner relationship (4.3%). Although most fears related to fetal well-being, other concerns included problems with family members, women’s own mental health, and not being a “good mother.” These findings support calls for antenatal education to more adequately address women’s psychosocial concerns.

• [x] Quantifying the Effects of COVID-19 on Restaurant Reviews (NLP for Social Media Workshop 2021) This is a good paper showing how to look at differences before and during COVID

  The COVID-19 pandemic has implications beyond physical health, affecting society and economies. Government efforts to slow down the spread of the virus have had a severe impact on many businesses, including restaurants. Mandatory policies such as restaurant closures, bans on social gatherings, and social distancing restrictions have affected restaurant operations as well as customer preferences (e.g., prompting a demand of stricter hygiene standards). As of now, however, it is not clear how and to what extent the pandemic has affected restaurant reviews, an analysis of which could potentially inform policies for addressing this ongoing situation. In this work, we present our efforts to understand the effects of COVID-19 on restaurant reviews, with a focus on Yelp reviews produced during the pandemic for New York City and Los Angeles County restaurants. Overall, we make the following contributions. First, we assemble a dataset of 600 reviews with manual annotations of fine-grained COVID-19 aspects related to restaurants (e.g., hygiene practices, service changes, sympathy and support for local businesses). Second, we address COVID-19 aspect detection using supervised classifiers, weakly-supervised approaches based on keywords, and unsupervised topic modeling approaches, and experimentally show that classifiers based on pre-trained BERT representations achieve the best performance (F1=0.79). Third, we analyze the number and evolution of COVID-related aspects over time and show that the resulting time series have substantial correlation (Spearman’s 𝜌=0.84) with critical statistics related to the COVID-19 pandemic, including the number of new COVID-19 cases. To our knowledge, this is the first work analyzing the effects of COVID-19 on Yelp restaurant reviews and could potentially inform policies by public health departments, for example, to cover resource utilization.

[daphnaspira]

Narrative Paths and Negotiation of Power in Birth Stories

• Looked at online birth stories to see how the r/BabyBumps community frames birth narratives.
• Good data because the stories are unsolicited and from the person giving birth's point of view so it's an honest narrative not often documented in healthcare.
• Stories are unique but there is a predictable sequence to the stories across the dataset. This sequence/structure also generally follows the biological process of giving birth as documented in medical literature.
• Collected 2,847 stories from r/BabyBumps. Only posts that were >500 words long. Took out comments & interactions with the post.
• Selected eight labels about the birth stories (negative, positive, medicated, unmedicated, first, second, c-section, vaginal). A story can have multiple labels. Positive means that it was framed positively, not that it wasn't negative.
• Measured "typical" and "outlier" narratives within the dataset to see which types of birth stories the community normalizes and which stories are considered unusual.
• Used VADER to find sentiment analysis over the course of the stories (each broken into 10 sections).
• Found main personas who appear in the stories
• Used LDA to topic model 50 topics using 100 word chunks of stories, found probability of transitions between topics
• Determined the appearance of the topics and the personas over the course of the stories on average.
• Clear patterns throughout the story sequences.
• Measured personas power in the birth stories by lemmatizing verbs and using spaCy to parse the stories.
• Found that the stories framed authors (person giving birth) as disempowered and that the medical professionals and doulas were the most empowered.
• Important ethical considerations about not sharing this unsolicited data so they don't share the data and paraphrase all the stories included. Released the labeling pipeline to help other researchers replicate their results.

[daphnaspira]

Fathers’ Worries During Pregnancy: A Qualitative Content Analysis of Reddit

• Looked at r/predaddit to look at fathers' worries during pregnancy period
• four main categories of worries: infant factors, partner factors, situational factors, individual factors
• 535 posts in final data set
• made word cloud of most commonly occurring words
• plotted most common fears by trimester: perinatal loss is most commonly cited fear in first two trimesters, labor/childbirth is most common in third trimester

[azpoliak]

Were the categories and fears manually labeled/coded?

On Tue, Jun 29, 2021 at 12:57 PM daphnaspira @.***> wrote:

Fathers’ Worries During Pregnancy: A Qualitative Content Analysis of Reddit

• Looked at r/predaddit to look at fathers' worries during pregnancy period
• four main categories of worries: infant factors, partner factors, situational factors, individual factors
• 535 posts in final data set
• made word cloud of most commonly occurring words
• plotted most common fears by trimester: perinatal loss is most commonly cited fear in first two trimesters, labor/childbirth is most common in third trimester

— You are receiving this because you authored the thread. Reply to this email directly, view it on GitHub https://github.com/azpoliak/birthing_experiences/issues/2#issuecomment-870873832, or unsubscribe https://github.com/notifications/unsubscribe-auth/AA3XBFVX2KW5TJPZDQEVQA3TVIQRZANCNFSM47IHTIPQ .

[daphnaspira]

Were the categories and fears manually labeled/coded? … On Tue, Jun 29, 2021 at 12:57 PM daphnaspira @.**> wrote: Fathers’ Worries During Pregnancy: A Qualitative Content Analysis of Reddit* - Looked at r/predaddit to look at fathers' worries during pregnancy period - four main categories of worries: infant factors, partner factors, situational factors, individual factors - 535 posts in final data set - made word cloud of most commonly occurring words - plotted most common fears by trimester: perinatal loss is most commonly cited fear in first two trimesters, labor/childbirth is most common in third trimester — You are receiving this because you authored the thread. Reply to this email directly, view it on GitHub <#2 (comment)>, or unsubscribe https://github.com/notifications/unsubscribe-auth/AA3XBFVX2KW5TJPZDQEVQA3TVIQRZANCNFSM47IHTIPQ .

It's not totally clear but it seems manual.

[daphnaspira]

Quantifying the Effects of COVID-19 on Restaurant Reviews

• Looked at changes in restaurant reviews before and during COVID
• Analyzed 3 million Yelp reviews from NYC and LA between Jan 1, 2019 and Dec 31, 2020 and made dataset of 600 reviews
• Manually annotated for mentions of aspects related to COVID if review contained 1+ COVID keyword
• COVID keywords: hygiene, transmission, social distancing, racism, sympathy and support, service, other
• randomly selected 400 reviews from Mar 1, 2020 onward that had COVID-related keywords and 200 from the same time period that didn't have the keywords
• Trained LDA topic model on different numbers of topics and manually named topics based on highest probability words for each topic
• trained two types of classifiers: one weakly supervised and one fully supervised to classify reviews with COVID aspects
• weakly supervised: manually defined dictionary of words for each of the COVID keywords and used teacher-student architecture to predict COVID or Non-COVID label based on probabilities of keyword appearances. They tried both bag-of-words and BERT as their student classifiers.
• used Prophet time series model to see change over time. Trained on pre-pandemic data and then checked degree of change for COVID-era reviews
• found correlations between elements of reviews and COVID timeline

[daphnaspira]

We aren't able to access the Mothers’ Worries During Pregnancy: A Content Analysis of Reddit paper

[azpoliak]

Ask Cait to send you a pdf copy. I can probably much later tonight

On Thu, Jul 1, 2021 at 11:15 AM daphnaspira @.***> wrote:

We aren't able to access the Mothers’ Worries During Pregnancy: A Content Analysis of Reddit paper

— You are receiving this because you authored the thread. Reply to this email directly, view it on GitHub https://github.com/azpoliak/birthing_experiences/issues/2#issuecomment-872452445, or unsubscribe https://github.com/notifications/unsubscribe-auth/AA3XBFRVV6VGAVLN3WZF5ZLTVSWCNANCNFSM47IHTIPQ .