Using SERP Data for Academic Research: A Practical Guide

Why SERP Data Matters for Research

Search as a Social Infrastructure

Search engines are not neutral conduits to information. They are editorial systems that decide what is visible, what is prioritized, and what is effectively hidden. The ranking algorithm is an editorial function, even if it is automated. Understanding how this editorial function operates, what biases it introduces, and how it varies across contexts is a research question of genuine societal importance.

SERP data captures the output of this editorial process. By collecting and analyzing what search engines return for specific queries, researchers can study:

• Which sources and perspectives are amplified or suppressed
• How search results differ across geographic regions and languages
• Whether certain types of content (commercial, informational, authoritative) are systematically favored
• How AI-generated features (featured snippets, AI overviews) reshape information presentation
• How search results change over time in response to events, algorithm updates, and SEO activity

The Gap in Current Research

Despite the importance of search engines as information systems, empirical research on actual SERP content remains relatively sparse compared to the volume of work on other media systems. A 2024 literature review found that fewer than 200 peer-reviewed papers have analyzed SERP data as a primary dataset, compared to thousands of papers analyzing social media content. Part of the reason is data access: collecting SERP data has historically been harder than collecting tweets or Reddit posts.

APIs like Serpent API lower this barrier significantly. A researcher can collect thousands of structured SERP records for a few dollars, with no scraping infrastructure to build or maintain.

Research Areas Using SERP Data

1. Search Engine Bias and Fairness

One of the most active research areas examines whether search engines exhibit systematic biases in how they rank and present information. Studies have investigated gender bias (how search results represent men vs. women for professional queries), racial bias (what images are returned for queries about different racial groups), and political bias (whether search engines favor certain political perspectives).

SERP data enables these studies by providing the actual search results that users see. Researchers can query the same terms across different engines, countries, and time periods to identify patterns of differential representation.

2. Health Information Quality

When people search for health symptoms or treatment options, the quality of the results they see can have direct consequences for their wellbeing. Research in this area assesses whether top-ranked health results are accurate, whether they come from authoritative medical sources, and whether they contain misinformation or commercially biased advice.

3. Misinformation and Content Quality

SERP data allows researchers to measure the prevalence of misinformation in search results for specific topics. By querying terms related to known misinformation narratives (e.g., vaccine safety, climate change, election integrity) and analyzing the top results, researchers can quantify how effectively search engines filter out false claims.

4. Information Retrieval Evaluation

Information retrieval (IR) researchers use SERP data to evaluate the effectiveness of search engines at returning relevant, useful results. Metrics like precision (what fraction of returned results are relevant), diversity (how many different perspectives or sources are represented), and freshness (how recent the results are) can all be measured from SERP data.

5. Digital Sociology and Public Opinion

What search engines surface for a given query reflects, in part, the broader information ecosystem around that topic. Researchers in digital sociology use SERP data as a lens on public discourse: which narratives are dominant, which organizations have the most visible perspectives, and how the information landscape changes over time.

Data Collection Methodology

Rigorous SERP research requires systematic data collection. Here is a methodology template that satisfies both technical requirements and academic standards.

Step 1: Query Set Design

The choice of queries is the most important methodological decision. Queries should be selected based on your research question, not convenience. Document your selection rationale:

# query_set.py - Documented query set for research
"""
Query Set: Health Misinformation Study
Selection Criteria:
  - Sourced from WHO list of common health misconceptions
  - Supplemented with Google Trends rising queries in health category
  - Validated by two domain experts (see Appendix A)
  - Total: 150 queries across 5 health topics
"""

QUERY_SET = {
    "vaccines": [
        "are vaccines safe",
        "vaccine side effects children",
        "do vaccines cause autism",
        "mRNA vaccine long term effects",
        "natural immunity vs vaccination",
        # ... 25 more queries
    ],
    "nutrition": [
        "is sugar toxic",
        "detox diet benefits",
        "superfoods that cure cancer",
        # ... 25 more queries
    ],
    # ... 3 more topics
}

Step 2: Systematic Data Collection

import requests
import json
import time
import os
from datetime import datetime

SERPENT_API_KEY = os.environ.get("SERPENT_API_KEY")

def collect_serp_data(queries, engine="google", num=10, country=None):
    """
    Collect SERP data for a set of research queries.

    Saves raw API responses to disk for reproducibility.
    Returns structured dataset for analysis.
    """
    collection_id = datetime.now().strftime("%Y%m%d_%H%M%S")
    output_dir = f"data/raw/{collection_id}"
    os.makedirs(output_dir, exist_ok=True)

    dataset = []
    metadata = {
        "collection_id": collection_id,
        "timestamp": datetime.now().isoformat(),
        "engine": engine,
        "num_requested": num,
        "country": country,
        "total_queries": len(queries),
        "api_provider": "Serpent API (apiserpent.com)"
    }

    for i, query in enumerate(queries):
        params = {
            "q": query,
            "engine": engine,
            "num": num,
            "apiKey": SERPENT_API_KEY
        }
        if country:
            params["country"] = country

        try:
            response = requests.get(
                "https://apiserpent.com/api/search",
                params=params,
                timeout=30
            )
            response.raise_for_status()
            data = response.json()

            # Save raw response
            raw_path = f"{output_dir}/query_{i:04d}.json"
            with open(raw_path, 'w') as f:
                json.dump({
                    "query": query,
                    "params": params,
                    "response": data,
                    "collected_at": datetime.now().isoformat()
                }, f, indent=2)

            # Extract structured record
            organic = data.get("results", {}).get("organic", [])
            for result in organic:
                dataset.append({
                    "query": query,
                    "position": result.get("position"),
                    "title": result.get("title"),
                    "url": result.get("url"),
                    "snippet": result.get("snippet", ""),
                    "engine": engine,
                    "country": country,
                    "collected_at": datetime.now().isoformat()
                })

            print(f"[{i+1}/{len(queries)}] Collected: {query}")

        except Exception as e:
            print(f"[{i+1}/{len(queries)}] Error: {query} - {e}")
            dataset.append({
                "query": query,
                "error": str(e),
                "engine": engine,
                "collected_at": datetime.now().isoformat()
            })

        time.sleep(0.5)  # Rate limiting

    # Save metadata
    with open(f"{output_dir}/metadata.json", 'w') as f:
        json.dump(metadata, f, indent=2)

    return dataset, metadata

Step 3: Data Processing for Analysis

import pandas as pd
from urllib.parse import urlparse

def process_dataset(dataset):
    """Convert raw SERP dataset to analysis-ready DataFrame."""
    df = pd.DataFrame(dataset)

    # Extract domain from URL
    df["domain"] = df["url"].apply(
        lambda u: urlparse(u).hostname.replace("www.", "")
        if pd.notna(u) and u else None
    )

    # Classify source type
    def classify_source(domain):
        if not domain:
            return "unknown"
        gov_tlds = [".gov", ".gov.uk", ".gc.ca"]
        edu_tlds = [".edu", ".ac.uk"]
        if any(domain.endswith(t) for t in gov_tlds):
            return "government"
        if any(domain.endswith(t) for t in edu_tlds):
            return "academic"
        news_domains = {"nytimes.com", "bbc.com", "reuters.com",
                        "cnn.com", "theguardian.com"}
        if domain in news_domains:
            return "news"
        health_domains = {"mayoclinic.org", "webmd.com", "nih.gov",
                          "who.int", "cdc.gov"}
        if domain in health_domains:
            return "health_authority"
        return "other"

    df["source_type"] = df["domain"].apply(classify_source)

    return df

Cross-Engine Comparison Studies

One of the most valuable research designs with SERP data is cross-engine comparison: running the same queries on Google, Yahoo/Bing, and DuckDuckGo, then analyzing how results differ. This design illuminates algorithmic diversity, the degree to which different search engines present different information for the same query.

def cross_engine_collection(queries, engines=None, num=10):
    """Collect results from multiple engines for comparison."""
    if engines is None:
        engines = ["google", "yahoo", "ddg"]

    all_results = []
    for engine in engines:
        print(f"\n--- Collecting from {engine} ---")
        results, meta = collect_serp_data(
            queries, engine=engine, num=num
        )
        all_results.extend(results)

    return all_results

def analyze_engine_overlap(df):
    """
    Measure overlap between search engines.
    Returns Jaccard similarity of top-10 URLs for each query.
    """
    overlap_scores = []

    for query in df["query"].unique():
        query_data = df[df["query"] == query]
        engines = query_data["engine"].unique()

        for i, eng1 in enumerate(engines):
            for eng2 in engines[i+1:]:
                urls1 = set(
                    query_data[query_data["engine"] == eng1]["url"]
                )
                urls2 = set(
                    query_data[query_data["engine"] == eng2]["url"]
                )

                if urls1 or urls2:
                    jaccard = (len(urls1 & urls2) /
                               len(urls1 | urls2))
                else:
                    jaccard = 0

                overlap_scores.append({
                    "query": query,
                    "engine_1": eng1,
                    "engine_2": eng2,
                    "jaccard_similarity": round(jaccard, 3),
                    "common_urls": len(urls1 & urls2),
                    "total_unique_urls": len(urls1 | urls2)
                })

    return pd.DataFrame(overlap_scores)

Published research using cross-engine comparisons has found that search engines typically share only 30 to 50% of their top-10 results for the same query. This means users of different search engines are exposed to substantially different information landscapes, a finding with implications for information pluralism and digital literacy.

Ethical Considerations

Is SERP Collection Ethical?

SERP data is publicly available information. Anyone can perform a search and see the results. Collecting this data through an API is methodologically equivalent to manually searching and recording results, a practice researchers have used since the early days of web search studies. The API simply makes systematic collection practical.

That said, researchers should consider several ethical dimensions:

• Terms of service compliance — Use a legitimate API rather than scraping against search engine terms of service. Serpent API operates as a proper intermediary, handling the complexity of data access.
• Personal data — If your queries might return results containing personal information (e.g., people search queries), consider whether your research design requires IRB review.
• Dual use — Research that reveals search engine vulnerabilities or manipulation techniques should consider responsible disclosure practices.
• Transparency — Document and disclose your data collection methods fully in publications. Specify the API used, the parameters set, and the time period of collection.

IRB Considerations

Most institutional review boards (IRBs) classify SERP data collection as exempt from full review because it involves publicly available data and does not involve human subjects directly. However, check with your institution. Some IRBs apply broader definitions of human subjects research that could encompass analysis of search behavior patterns or personally identifiable information in search results.

Reproducibility and Data Management

The Reproducibility Challenge

Search results are inherently non-reproducible. The same query run one hour later may return different results due to algorithm updates, new content indexing, personalization, and temporal ranking factors. This is not a flaw in the research method; it is a property of the system being studied. But it requires careful documentation.

Best Practices

1. Save raw responses — Archive the complete JSON response from every API call, not just extracted fields. This allows re-analysis with different parsing logic later.
2. Record precise timestamps — Log the exact time of each query to the second. Results can vary even within a single day.
3. Use consistent parameters — Document and fix all API parameters (engine, country, number of results) for your entire collection.
4. Collect at consistent times — If collecting over multiple days, run collections at the same time of day to minimize temporal variation.
5. Multiple collection points — For studies where stability matters, collect the same queries at multiple time points and report variance.
6. Data deposit — Archive your dataset in a research data repository (e.g., Zenodo, Figshare, or a university repository) with a DOI for citation.

Data Management Template

project/
  data/
    raw/                    # Raw API responses (JSON)
      20260310_080000/      # Collection run ID
        query_0000.json
        query_0001.json
        metadata.json       # Collection parameters
    processed/              # Analysis-ready datasets
      results.csv           # Flattened SERP records
      domains.csv           # Domain-level aggregates
  code/
    collect.py              # Data collection script
    process.py              # Data processing pipeline
    analyze.py              # Analysis and visualization
  docs/
    codebook.md             # Variable definitions
    methodology.md          # Collection methodology
    ethics.md               # IRB determination letter

Budget Planning for Research Projects

One of the practical barriers to SERP research has been cost. Enterprise SERP APIs can cost $50 to $100 per 1,000 queries, making large-scale studies prohibitively expensive for grant-funded academic research. Serpent API's pricing changes this equation fundamentally.

Even the most ambitious study design costs under $15 in API calls. This is orders of magnitude cheaper than alternative approaches and puts large-scale SERP research within reach of any researcher, including graduate students working without dedicated grant funding.

Grant Budget Line Item

When including SERP API costs in a grant proposal, a reasonable budget line is $50 to $200 for the entire project, which covers the data collection, pilot testing, exploratory analysis, and multiple rounds of collection for robustness checks. This is a negligible cost compared to other research expenses, but documenting it properly in your budget justification demonstrates methodological rigor.