Exploring Research on Gender Equality with NLP and Elicit

Introduction

Gender equality is one of the most pressing social issues of our time, with far-reaching implications for economic development, public health, education, and human rights around the world. In recent years, there has been a growing body of research examining the state of gender equality in different countries and the factors influencing progress toward greater equality between men and women.

One country where gender equality has been a particularly prominent concern is Afghanistan. Historically, Afghan women and girls have faced severe restrictions on their rights and freedoms under patriarchal cultural norms and conservative religious interpretations. While some progress was made in expanding access to education, employment, and political participation for women after the fall of the Taliban regime in 2001, gender gaps have remained pervasive and the future of women‘s rights is again uncertain after the Taliban returned to power in 2021.

Given the complex and evolving situation in Afghanistan, there is a need for rigorous, data-driven research to understand the barriers to gender equality and identify effective strategies for closing gender gaps. However, the sheer volume of research being produced on this topic can make it challenging for scholars, policymakers, and practitioners to stay up-to-date with the latest findings and insights.

This is where natural language processing (NLP) and AI-powered literature review tools like Elicit can be incredibly valuable. NLP refers to a branch of artificial intelligence focused on enabling computers to understand, interpret, and generate human language. By applying NLP techniques to large collections of research papers, it becomes possible to quickly identify key themes, trends, and findings without needing to read each individual paper.

Elicit is a free online platform that leverages NLP and machine learning to help researchers find relevant papers and extract key information for literature reviews. Users can search for papers by asking questions in natural language, and Elicit will scan its database of over 175 million articles to find relevant results. Elicit also allows users to upload their own papers for analysis and supports data export for further exploration.

In this blog post, we will demonstrate how NLP and Elicit can be used to efficiently analyze a large body of research on gender equality in Afghanistan. We will walk through the process of using Elicit to search for relevant papers and download the results as a CSV file, which we will then analyze using Python and popular data science libraries like pandas, nltk, and matplotlib.

Through our analysis, we aim to showcase the power of NLP and AI tools for synthesizing knowledge and generating insights from large collections of research papers. We will also reflect on the benefits and limitations of these approaches and discuss opportunities for further research and analysis.

Searching for Research Papers with Elicit

To begin our analysis, we will use Elicit to search for research papers related to gender equality in Afghanistan. We start by going to the Elicit website (elicit.org) and entering our search query into the search bar: "Why did gender equality fail in Afghanistan?"

Elicit quickly returns a list of relevant papers from its database, sorted by relevance. We can scroll through the results and click on individual papers to view more details, including the title, authors, abstract, publication date, and journal.

To download the results for further analysis, we click the "Export CSV" button at the top of the search results page. This downloads a CSV file containing key metadata for each of the papers, including the title, URL, authors, abstract, year, journal, and number of citations.

Analyzing the Research Papers in Python

Now that we have our CSV file of research paper metadata, we can begin analyzing it using Python. We will use the pandas library to read in the CSV file as a dataframe and perform various data exploration and visualization tasks.

First, let‘s read in the CSV file and take a look at the structure of the dataframe:

import pandas as pd

# Read in the CSV file
papers_df = pd.read_csv(‘elicit_results.csv‘)

# Display the first few rows of the dataframe
papers_df.head()

The dataframe has 168 rows, corresponding to the 168 research papers returned by our Elicit search query. There are 15 columns containing metadata for each paper, such as the title, authors, abstract, year of publication, and number of citations.

Next, let‘s explore how the research on gender equality in Afghanistan has evolved over time by plotting the distribution of papers by publication year:

import matplotlib.pyplot as plt
import seaborn as sns

# Create a count plot of papers by year
fig, ax = plt.subplots(figsize=(10,6))
sns.countplot(x=‘year‘, data=papers_df, color=‘steelblue‘)

ax.set_xlabel(‘Publication Year‘)
ax.set_ylabel(‘Number of Papers‘)
ax.set_title(‘Distribution of Gender Equality Research Papers by Year‘)

plt.show()

The plot shows a clear increase in the number of papers published on this topic over time, with a particularly sharp rise in the late 2010s. This suggests that gender equality in Afghanistan has become an increasingly important focus of research in recent years, likely driven by ongoing challenges and setbacks for women‘s rights in the country.

To dig deeper into the content of the research papers, we can analyze the text data contained in the abstracts. However, before we can perform meaningful analysis, we need to preprocess the text data to clean and standardize it.

First, let‘s apply some common text preprocessing steps to the abstract data:

import nltk
from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer

# Download required NLTK resources
nltk.download(‘punkt‘)
nltk.download(‘stopwords‘)
nltk.download(‘wordnet‘)

# Convert abstracts to lowercase
papers_df[‘abstract‘] = papers_df[‘abstract‘].str.lower()

# Tokenize abstracts into individual words
papers_df[‘abstract_tokens‘] = papers_df[‘abstract‘].apply(nltk.word_tokenize)

# Remove stop words
stop_words = set(stopwords.words(‘english‘))
papers_df[‘abstract_tokens‘] = papers_df[‘abstract_tokens‘].apply(lambda x: [word for word in x if word not in stop_words])

# Lemmatize words
lemmatizer = WordNetLemmatizer()
papers_df[‘abstract_tokens‘] = papers_df[‘abstract_tokens‘].apply(lambda x: [lemmatizer.lemmatize(word) for word in x])

# Remove words shorter than 3 characters
papers_df[‘abstract_tokens‘] = papers_df[‘abstract_tokens‘].apply(lambda x: [word for word in x if len(word) > 2])

In this code snippet, we first convert all the abstracts to lowercase to standardize the text. We then tokenize each abstract into a list of individual words using NLTK‘s word_tokenize() function.

Next, we remove stop words, which are common words like "the", "and", "of" that don‘t contribute much meaning. We use NLTK‘s built-in list of English stop words for this.

We then apply lemmatization to each word to reduce words to their base or dictionary form (e.g. "running" becomes "run"). This helps standardize words with the same meaning but different inflections.

Finally, we remove any remaining words that are shorter than 3 characters, as these are likely to be noise rather than meaningful words.

With our text data preprocessed, we can now analyze the most frequently occurring words across all the abstracts. One way to visualize this is by creating a word cloud:

from wordcloud import WordCloud

# Combine all the abstracts into a single string
abstract_texts = ‘ ‘.join(papers_df[‘abstract‘])

# Create a word cloud
wordcloud = WordCloud(width=800, height=400, background_color=‘white‘).generate(abstract_texts)

# Plot the word cloud
fig, ax = plt.subplots(figsize=(10,6))
ax.imshow(wordcloud, interpolation=‘bilinear‘)
ax.axis(‘off‘)
plt.show()

The word cloud highlights some of the key themes and topics that appear frequently in the abstracts, such as "women", "rights", "education", "violence", "conflict", and "development". This provides a high-level overview of the main focus areas of the research papers.

To get a more nuanced understanding of the language used in the abstracts, we can apply sentiment analysis to quantify the overall emotional tone. One popular tool for this is the VADER (Valence Aware Dictionary and sEntiment Reasoner) sentiment analyzer:

from nltk.sentiment import SentimentIntensityAnalyzer

# Download the VADER lexicon
nltk.download(‘vader_lexicon‘)

# Initialize the sentiment analyzer
sid = SentimentIntensityAnalyzer()

# Apply the analyzer to each abstract
papers_df[‘sentiment_scores‘] = papers_df[‘abstract‘].apply(lambda x: sid.polarity_scores(x))

# Extract the compound sentiment score
papers_df[‘sentiment_score‘] = papers_df[‘sentiment_scores‘].apply(lambda x: x[‘compound‘])

Here, we use NLTK‘s SentimentIntensityAnalyzer to compute sentiment scores for each abstract. The analyzer returns a set of scores for each text, including positive, negative, and neutral scores, as well as a compound score that summarizes the overall sentiment on a scale from -1 (very negative) to 1 (very positive).

We extract the compound sentiment score and add it as a new column to our dataframe. We can then visualize the distribution of sentiment scores:

fig, ax = plt.subplots(figsize=(8,5))
sns.histplot(data=papers_df, x=‘sentiment_score‘, color=‘steelblue‘, kde=True)

ax.set_xlabel(‘Sentiment Score‘)
ax.set_ylabel(‘Count‘)
ax.set_title(‘Distribution of Sentiment Scores in Abstracts‘)

plt.show()

The plot shows that the sentiment scores are roughly normally distributed, with a mean around 0 (neutral). This suggests that the language used in the abstracts is generally objective and unemotional, as we would expect for academic research papers.

However, there is a slight skew toward positive sentiment scores, indicating that the research on gender equality in Afghanistan tends to have a somewhat optimistic or hopeful tone overall, despite documenting many challenges and setbacks.

We can also examine how the sentiment of the research has changed over time:

fig, ax = plt.subplots(figsize=(10,6))
sns.lineplot(x=‘year‘, y=‘sentiment_score‘, data=papers_df)

ax.set_xlabel(‘Year‘)
ax.set_ylabel(‘Sentiment Score‘) 
ax.set_title(‘Sentiment of Gender Equality Research Over Time‘)

plt.show()

The line plot shows an interesting pattern: the sentiment of the research becomes increasingly positive over time, particularly after the early 2000s. This could reflect a growing sense of progress and optimism about the state of women‘s rights in Afghanistan following the fall of the Taliban regime in 2001.

However, it‘s important to note that sentiment analysis is an imperfect tool and may not always accurately capture the nuance and context of language. The VADER analyzer, like other sentiment analysis tools, relies on predefined dictionaries of positive and negative words, which may not always align with the specialized language and terminology used in academic research.

Limitations and Future Directions

While NLP and tools like Elicit offer powerful ways to quickly analyze large collections of research papers, they also have some limitations. One key issue is that these tools rely heavily on the quality and representativeness of the underlying data. If certain perspectives or types of research are underrepresented in the databases used by tools like Elicit, the insights generated may be biased or incomplete.

Additionally, NLP techniques like sentiment analysis and topic modeling can sometimes struggle to capture the full complexity and nuance of language, especially in domain-specific contexts like academic research. It‘s important to interpret the outputs of these tools carefully and critically, and to triangulate insights with other forms of analysis and expert knowledge.

Despite these limitations, NLP and AI-powered literature review tools have enormous potential to accelerate and scale up research synthesis efforts. Some promising future directions include:

• Expanding the scope of databases used by tools like Elicit to include a wider range of research sources, including non-English language publications, grey literature, and primary data

• Developing domain-specific NLP models and sentiment analyzers that are tailored to the language and terminology used in particular research fields

• Integrating other types of data beyond text, such as images, videos, and structured datasets, to enable more comprehensive and multimodal analysis

• Combining NLP techniques with network analysis to map out the relationships between different researchers, institutions, and ideas within a research field

• Applying more advanced NLP techniques like argument mining and cause-effect extraction to automatically identify key claims and evidence from research papers

• Creating user-friendly interfaces and visualizations to make insights from NLP and AI-powered literature reviews more accessible and actionable for researchers, policymakers, and the public

Ultimately, the goal of using NLP and AI tools for research synthesis should be to augment and scale up human expertise, not to replace it entirely. By leveraging the power of these tools to efficiently process large amounts of information and uncover hidden patterns and insights, researchers can focus their efforts on higher-level sensemaking, critical analysis, and creative exploration of new ideas and solutions.

Conclusion

In this blog post, we demonstrated how NLP techniques and AI-powered literature review tools like Elicit can be used to quickly analyze a large collection of research papers on gender equality in Afghanistan. By applying text preprocessing, sentiment analysis, and visualization techniques, we were able to identify key themes, trends, and patterns in the research literature, including the increasing volume of research over time, the main topics of focus, and the generally objective but slightly optimistic sentiment of the research.

While NLP and AI tools have limitations and should be used carefully and critically, they offer enormous potential to accelerate and scale up research synthesis efforts. By leveraging these tools to efficiently process large amounts of information and uncover hidden insights, researchers can focus their efforts on higher-level sensemaking and creative exploration of new ideas and solutions.

As the volume and complexity of research continues to grow, NLP and AI will likely play an increasingly important role in helping researchers stay up-to-date and synthesize knowledge across disciplines. By combining the power of these tools with human expertise and judgment, we can work towards more efficient, comprehensive, and impactful research that advances our understanding of complex social issues like gender equality.