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Webinar: Web-scraping for Social Science Research: APIs as a Source of Data
30th April 2020 @ 8:00 am - 5:00 pm
30 April 2020
|Organised by||UK Data Service|
|Presenter||Dr Diarmuid Mcdonnell|
|Description||Vast swathes of our social interactions and personal behaviours are now conducted online and/or captured digitally. In addition to common sources such as social media/network platforms and text corpora, websites and online databases contain rich information of relevance to social science research. Thus, computational methods for collecting data from the web are an increasingly important component of a social scientist’s toolkit.
This free webinar, organised by the UK Data Service, is the third in a series of three on how to collect data from the web using computational methods. Specifically, this webinar delineates the value, logic and process of capturing data stored in online databases through an API (application programming interface). Presented by Dr Diarmuid McDonnell of the UK Data Service, this webinar will cover the step-by-step process of downloading data via an API, including providing sample code written in the popular Python programming language. It demonstrates techniques for downloading public information on the Covid-19 pandemic, as well as for a range of other social science subjects (e.g., crime data via the Police UK API, business information via the Companies House API).
Webinar one, on 27 March, will provide an example of a published piece of social research that utilised web-scraping techniques to generate a novel, linked administrative dataset to evaluate a regulatory intervention.
Webinar two, on 23 April, will demonstrate how to scrape information from web pages using the Python programming language.
There is also a parallel webinar series focusing on getting, storing and manipulating data that illustrates a variety of complementary techniques for collecting data from the web.
|Level||Entry (no or almost no prior knowledge)|
|Website and registration||
|Keywords||Data Collection, Data Quality and Data Management , Mixed Methods Data Handling and Data Analysis|
|Related publications and presentations||Data Collection
Data Quality and Data Management
Mixed Methods Data Handling and Data Analysis