Just sharing stuff…
Manila, Philippines. The past few days/weeks, we have been seeing a lot of posts on our fB walls and Twitter feeds on #AlDub. AlDub is an “accidental” segment on Eat Bulaga that had people following the program for a little over a month now. I haven’t been able to follow the craze since I only have TFC here abroad, but what I did instead was follow the tweets.
In the plots below, we count the number of tweets recorded for each of the terms for every hour of everyday for the entire month of August.
In the first plot, we only look at the tweets with the patterns “eatbulaga” and “showtime” in them. Eat Bulaga and It’s Showtime are two primetime shows of the two rival TV networks in the Philippines. From the plot, we can see that there are more tweets associated with “showtime” (“ItsShowTime“) than “eatbulaga”. This has been the trend (on TwitterWorld) for quite some time now. (Note that it is highly likely that this distribution of “televiewers” is skewed, i.e.
Viewers != Twitter People)
Then the #AlDub phenomenon happened. The way people have received this 30-minute Eat Bulaga segment makes it more like a TV program in itself. Look at the trends below. The graph is the same as the plot above, but this time, we added the “AlDub” curve (blue). The difference is stark! One can speculate that #AlDub is actually carrying the entire Eat Bulaga show. If this dynamics can be correlated to TV ratings, we know that It’s Showtime is in deep trouble. The next question is, how long can #AlDub keep things up for Eat Bulaga?
The huge spike in the number of recorded tweets is especially interesting. What happened on 22 August 2015? Googling “aldub august 22 2015” hints to the episode titled “The Wedding of Frankie and Yaya Dub”. This was also the day when the hashtag #ALDUBAgainstALLODDS trended worldwide. This is in fact captured in the word cloud below.
We further filter the tweets into those that have geotagging, i.e. the tweets have associated locations with them. This, of course, limits the data points in the set since not all Twitterzens in the Philippines have activated their location trackers. Be that as it may, below is a cartodb visualization of the tagged tweets as they are being tweeted across the Philippines.
As I am usually the last to know about current trends on Philippine Showbiz, I wasn’t able to collect tweets that are specifically on the topic; nevertheless, I still have a rich collection of non-topic-specific tweets. I have been continuously mining streamed tweets (thanks to DigitalOcean for the paid cloud service) originating from the Philippines. Unfortunately, I only have the Twitter spritzer access, which only allows mining of 1% of randomly sampled public tweets for free. The other two types of access are: gardenhose (10%) and firehose (100%). In any case, I was still able to see some interesting patterns after filtering in tweets (from my very limited collection) that involve any one of the following words: aldub, showtime, eatbulaga, and adviceganda.
The SQL statement I used to pull the tweets is shown below.
fname = "mytweets.sqlite" con = sq.connect(fname) df = pd.read_sql('''SELECT TWEET, CREATED_AT, LATITUDE, LONGITUDE, USER_ENTITIES_HASHTAG FROM PHTWEETS WHERE (TWEET LIKE "%showtime%") OR (TWEET LIKE "%aldub%") OR (TWEET LIKE "%eatbulaga") OR (TWEET LIKE "%adviceganda%") COLLATE NOCASE''', con) con.close()
Notice the “%” symbol in the SQL statement. The symbol is a placeholder for “wildcards” (missing letters/words). In the statement, the symbol is placed before and after the terms we are interested in. For example, a tweet that has the word “ItsShowtime” or “ItsShowtimeNa” will be collected since it qualifies under the “%showtime%” filter.