Scrape author publication timelines

To scrape author publication timelines, you’ll typically want to extract data from online sources where authors’ works are indexed. This can include academic databases, publishing platforms, or personal websites. Here’s a breakdown of how you can approach this:

1. Define Your Target Sources

Some common sources include:

• Google Scholar: Academic author profiles with publication lists and years.

• ResearchGate or ORCID: Author timelines and publication metadata.

• PubMed, IEEE Xplore, ACM Digital Library: Great for specific fields.

• Amazon, Goodreads: For book authors.

• Medium, Substack, Blogs: For online article authors.

2. Tools & Libraries for Web Scraping

Use a combination of the following:

• requests – for sending HTTP requests

• BeautifulSoup – for parsing HTML

• Selenium – for interacting with JavaScript-heavy pages

• Scrapy – for building scalable scrapers

• pandas – for organizing timeline data

3. General Scraping Workflow

python
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import requests
from bs4 import BeautifulSoup
import pandas as pd

def get_google_scholar_timeline(author_url):
    headers = {"User-Agent": "Mozilla/5.0"}
    response = requests.get(author_url, headers=headers)
    soup = BeautifulSoup(response.text, "html.parser")

    publication_data = []
    for entry in soup.select(".gsc_a_tr"):
        title = entry.select_one(".gsc_a_at").text
        year_tag = entry.select_one(".gsc_a_y span")
        year = year_tag.text if year_tag else "N/A"
        publication_data.append((title, year))
    
    return pd.DataFrame(publication_data, columns=["Title", "Year"])

4. Handling JavaScript-Rendered Pages (e.g., Medium or Substack)

python
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from selenium import webdriver
from selenium.webdriver.chrome.options import Options

def scrape_medium_author_timeline(author_url):
    options = Options()
    options.add_argument("--headless")
    driver = webdriver.Chrome(options=options)
    driver.get(author_url)

    articles = driver.find_elements_by_css_selector("article")
    timeline = []
    for article in articles:
        try:
            title = article.find_element_by_tag_name("h2").text
            date = article.find_element_by_tag_name("time").get_attribute("datetime")
            timeline.append((title, date[:10]))
        except:
            continue
    
    driver.quit()
    return pd.DataFrame(timeline, columns=["Title", "Date"])

5. Considerations

• Rate Limiting & Ethics: Avoid sending too many requests. Respect robots.txt.

• Legal Compliance: Ensure the site allows scraping or offers APIs.

• Anti-bot Measures: Handle captchas, session tokens, and rotating user agents.

6. Alternative: Use APIs

Whenever possible, prefer APIs:

• Crossref API – Academic metadata.

• Google Books API – Author book lists.

• Goodreads API (now limited).

• OpenAlex API – Alternative to Microsoft Academic.

Example with Crossref:

python
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import requests

def get_crossref_timeline(author_name):
    url = f"https://api.crossref.org/works?query.author={author_name}&rows=1000"
    r = requests.get(url)
    items = r.json().get("message", {}).get("items", [])
    timeline = [(i["title"][0], i.get("published-print", {}).get("date-parts", [[None]])[0][0]) for i in items if "title" in i]
    return pd.DataFrame(timeline, columns=["Title", "Year"])

7. Visualizing the Timeline

Once scraped, visualize with matplotlib or plotly:

python
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import matplotlib.pyplot as plt

def plot_timeline(df):
    df["Year"] = pd.to_numeric(df["Year"], errors='coerce')
    df = df.dropna()
    counts = df["Year"].value_counts().sort_index()
    counts.plot(kind="bar")
    plt.title("Publication Timeline")
    plt.xlabel("Year")
    plt.ylabel("Number of Publications")
    plt.show()

Let me know the specific platform or author source, and I can tailor code or instructions accordingly.