Data harvesting is the entry point for any social media analysis project. There are two main ways to collect data for an analysis: by connecting to APIs or by crawling and scraping the social networks. It is crucial to understand how the data was collected in order to be aware of the bias that might introduced. Different harvesting techniques will require customized approaches to data preprocessing and analysis workflow, which we will explain in further chapters.
A widely used term, API (Application Programming Interface) is defined as a set of instructions and standards to access a web based software application. But what does it mean in real life? Firstly, APIs allow users to send a request for a particular resource, such as Facebook or Twitter , and receive some data in response. It is worth noting that all API providers fix some limitations on the quantity or type of data which users can obtain. APIs give access data processing resources, such as AlchemyAPI that receives in a request verbatim (textual data) and sends in response all results of the analysis, such as nouns, verbs, entities, and so on. In our case, the APIs are used either to get data from social networks or to execute some complex processing on them.
In order to access and manipulate APIs, we have to install the urllib2 library:
pip3 install urllib2In some cases, if you fail to perform the installation. You can also try using the request library, which is compatible with Python 2.x and 3.x.
pip3 install requestScraping (or web scraping) is a technique to extract information from websites. When we do not have access to APIs, we can only retrieve visible information from HTML generated on a web page. In order to perform the task, we need a scraper that is able to extract information that we need and structure it in a predefined format. The next step is to build a crawler-€”a tool to follow links on a website and extract the information from all sub pages. When we decide to build a scraping strategy, we have to take into consideration the terms and conditions, as some websites do not allow scraping.
Python offers very useful tools to create scrapers and crawlers, such as beautifulsoup and scrapy.
pip3 install bs4, scrapy