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title: webbotparseR - Parse html files containing search engine results
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format:
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html:
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embed-resources: true
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gfm: default
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---
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## Description
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# webbotparseR - Parse html files containing search engine results
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<!-- - Provide a brief and clear description of the method, its purpose, and what it aims to achieve. Add a link to a related paper from social science domain and show how your method can be applied to solve that research question. -->
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## Description
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<!--
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1. Provide a brief and exact description of the method clearly mentioning its purpose i.e., what the method does or aims to achieve in abstract terms (avoiding technical details).
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2. The focus should be on explaining the method in a way that helps users with different levels of expertise understand what it does, without going into technical details. It should clearly describe what inputs are needed and what outputs can be expected.
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Parse search engine results which have been scraped with the 'WebBot' browser extension <https://github.com/gesiscss/WebBot>.
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@@ -20,40 +46,59 @@ Parse search engine results which have been scraped with the 'WebBot' browser ex
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* Search Engine Results
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* Data Preprocessing
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## Science Usecase(s)
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## Use Cases
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Search engine results data is a valuable resource for research, enabling the study of information-seeking behaviors and the broader impact of search algorithms on society. By analyzing queries and result rankings, researchers can investigate how individuals interact with information and how biases in search algorithms influence knowledge acquisition and decision-making. This data is essential for examining phenomena like the spread of misinformation, the creation of filter bubbles, and the public's access to diverse perspectives. Additionally, search data can reveal temporal trends in societal concerns and interests, providing insights into collective behavior during events like elections, crises, or cultural moments. Leveraging techniques such as query classification and network analysis, researchers can explore the interplay between user intent, algorithmic curation, and societal outcomes, contributing to a better understanding of digital information ecosystems.
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## Repository structure
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## Input Data
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<!--
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1. The input data section should illustrate the input data format by showing a (possibly abbreviated) example item and explaining (or linking to an explanation of) the data fields.
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This repository follows [the standard structure of an R package](https://cran.r-project.org/doc/FAQ/R-exts.html#Package-structure).
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The package accepts data that has been gathered with the 'WebBot' browser extension <https://github.com/gesiscss/WebBot>. It also features a sample dataset of search engine results.
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## Output Data
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1. The output data section should illustrate the output data format by showing a (possibly abbreviated) example item and explaining (or linking to an explanation of) the data fields.
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## Hardware Requirements
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webbotparseR runs on any hardware that can run R.
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## Environment Setup
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With R installed:
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```r
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install.packages("webbotparseR")
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```
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## Input Data
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<!-- - The input data has to be a Digital Behavioral Data (DBD) Dataset -->
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<!-- This is an example -->
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The package accepts data that has been gathered with the 'WebBot' browser extension <https://github.com/gesiscss/WebBot>.
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## Sample Input and Output Data
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## Repository structure
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The package contains a sample dataset of search engine results.
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This repository follows [the standard structure of an R package](https://cran.r-project.org/doc/FAQ/R-exts.html#Package-structure).
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## How to Use
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<!--## References -->
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<!-- ## Publication -->
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## Contact Details
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