azpoliak / birthing_experiences Goto Github PK

• 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.

• 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.

• 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.

• src/:

  • Under src/ create a directory for each analysis method and move the corresponding python files there:

  • topic-modeling

  • `sentiment

  • personas

  • get rid of imports.py and import python libraries that are necessary in each python file directly.

  • get rid of data files that shouldnt be here.

• data/

• We'll clean this up later

Create the following for the entire BabyBumps dataset collected (more than just whats in the paper)

• Table 1 corpus stats

• Figure 1(left) - how many stories appeared in a year

• Figure 1(right) - number of words in a story

• Apply labels to each story based on Table 3

• Figure 2 - sentiment over narrative time

• Figure 3 - topic modeling

• Personas:

  • Table 5 - prevalence of the personas in the corpus
  • Figure 4 - persona presence throughout the story

• There are two environment yaml files in the root of the repo. Let's just have one.
• Move non *.ipynb files out of /src/notebooks/ and just to src/
• remove all_births.pkl. Its not good to push files that are over 5MB to the remote server. Also, its better to store the data as a compressed json file rather than a pickle file.
• In the *.py files, use a main function instead of writing out all the code sequentially. Here's a good overview of main functions in python from realpython.com

Topic Modeling

• In topic modeling, when saving the figures to a file, it might be good to also include the topic's first few words in the file name. This will spare us from having to open all the figures to know which topic it is about
• don't hard code how many topics are saved in the plots. I think this can be inferred by the shape of the dataframe.

  birthing_experiences/src/notebooks/Topic_Modeling.py

  Line 146 in 1ce7f64

  for i in range(50):

Code Duplication

Instead of copying the code from the different files, like sentiment, labeling, etc into Maria_paper.py, you can access the functions from those *.py files by importing them directly. We can go over this tomorrow.

• it would be similar to this example where we imported the classes in this example of Object Oriented Programing in python

• geolocation
• prophet time projections for topics
• bar graph for home/hospital
• adding line for topic probabilities
• average story length of pre v post
• comparison of healthcare providers
• statistical analysis for meaningful differences in personas
• statistical analysis for prophet time projections
• statistical analysis for sentiments
• sentiment for four sections
• google sheet random sample 20 stories each to look at
• • what is happening in the story during 0.6-0.9 time frame for home v hospital
• topics in different subreddits generally pre vs post covid
• redo the stats for personas total stories