Twitter数据抓取的方法(一)

Scraping Tweets Directly from Twitters Search Page – Part 1

Published January 8, 2015

EDIT – Since I wrote this post, Twitter has updated how you get the next list of tweets for your result. Rather than using scroll_cursor, it uses max_position. I’ve written a bit more in detail here.

In fairly recent news,
Twitter has started indexing it’s entire history of Tweets going all
the way back to 2006. Hurrah for data scientists! However, even with
this news (at time of writing), their search API is still restricted to
the past seven days of Tweets. While I doubt this will be the case
permanently, as a useful exercise this post presents how we can search
for Tweets from Twitter without necessarily using their API. Besides the
indexing, there is also the advantage that Twitter is a little more
liberal with rate limits, and you don’t require any authentication keys.

The post will be split up into two parts, this first part looking at
what we can extract from Twitter and how we might start to go about it,
and the second a tutorial on how we can implement this in Java.

Right, to begin, lets say we want to search Twitter for all tweets
related to the query “Babylon 5”. You can access Twitters advanced
search without being logged in: https://twitter.com/search-advanced

If we take a look at the URL that’s constructed when we perform the search we get:

https://twitter.com/search?q=Babylon%205&src=typd

As we can see, there are two query parameters, q (our query encoded) and src (assumed to be the source of the query, i.e. typed).  However, by default, Twitter returns top results, rather than all, so on the displayed page, if you click on All the URL changes to:

https://twitter.com/search?f=realtime&q=Babylon%205&src=typd

The difference here is the f=realtime parameter that appears to specify we receive Tweets in realtime as opposed to a subset of top Tweets. Useful to know, but currently we’re only getting the first 25 Tweets back. If we scroll down though, we notice that more Tweets are loaded on the page via AJAX. Logging all XMLHttpRequests in whatever dev tool you choose to use, we can see that everytime we reach the bottom of the page, Twitter makes an AJAX call a URL similar to:

https://twitter.com/i/search/timeline?f=realtime&q=Babylon%205&src=typd&include_available_features=1&include_entities=1&last_note_ts=85&scroll_cursor=TWEET-553069642609344512-553159310448918528-BD1UO2FFu9QAAAAAAAAETAAAAAcAAAASAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA

On further inspection, we see that it is also a JSON response, which is very useful! Before we look at the response though, let’s have a look at that URL and some of it’s parameters.

First off, it’s slightly different to the default search URL. The path is /i/search/timeline as opposed to /search. Secondly, while we notice our familiar parameters q, f, and src, from before, there are several additional ones. The most important and essential new one though is scroll_cursor. This is what Twitter uses to paginate the results. If you remove scroll_cursor from that URL, you end up with your first page of results again.

Now lets take a look now at the JSON response that Twitter provides:

{
has_more_items: boolean,
items_html: "...",
is_scrolling_request: boolean,
is_refresh_request: boolean,
scroll_cursor: "...",
refresh_cursor: "...",
focused_refresh_interval: int
}

Again, not all parameters for this post are important to take note of, but the ones that are include: has_more_items, items_html, and scroll_cursor.

has_more_items – This lets you know with a boolean value whether or not there are any more results after this query.

items_html – This is where all the tweets are which Twitter uses to append to the bottom of their timeline. It requires parsing, but there is a good amount of information in there to be extracted which we will look at in a minute.

scroll_cursor – A pagination value that allows us to extract the next page of results.

Remember our scroll_cursor parameter from earlier on? Well for each search request you make to twitter, the value of this key in the response provides you with the next set of tweets, allowing you to recursively call Twitter until either has_more_items is false, or your previous scroll_cursor equals the last scroll_cursor you had.

Now that we know how to access Twitters own search functionality, lets turn our attention to the tweets themselves. As mentioned before, items_html in the response is where all the tweets are at. However, it comes in a block of HTML as Twitter injects that block at the bottom of the page each time that call is made. The HTML inside is a list of li elements, each element a Tweet. I won’t post the HTML for one here, as even one tweet has a lot of HTML in it, but if you want to look at it, copy the items_html value (omiting the quotes around the HTML content) and paste it into something like JSBeautifier to see the formatted results for yourself.

If we look over the HTML, aside from the tweets text, there is actually a lot of useful information encapsulated in this data packet. The most important item is the Tweet id itself. If you check, it’s actually in the root li element. Now, we could stop here as with that ID, you can query Twitters official API, and if it’s a public Tweet, you can get all kinds of information. However, that’d defeat the purpose of not using the API, so lets see what we can extract from what we already have.

The table below shows various CSS selector queries that you can use to extract the information with.

That’s quite a sizeable amount of information in that HTML. From looking through, we can extract a bunch of stuff about the author, the time stamp of the tweet, the text, and number of retweets and favourites.

What have we learned here? Well, to summarize, we know how to construct a Twitter URL query, the response we get from said query, and the information we can extract from said response. The second part of this tutorial (to follow shortly) will introduce some code as to how we can implement the above.