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Observing Malaysian Social Media

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#GOP2012 Twitter Report Day 4

#GOP2012 is the official hashtag of the Republican National Convention currently being held at Tampa Bay, Florida from August 27th – August 30th. During this period we tracked mentions of #GOP2012, #RNC, #RNC2012, #RomneyRyan2012, #tcot, @GOPConvention,  @MittRomney and @PaulRyanVP on Twitter. Starting Day 3 we also tracked #WeBuiltIt.

The highest levels of interest were for Clint Eastwood (3954 tweets-per-minute), Mitt Romney (3852 tweets-per-minute) and the balloon drop/end of Romney’s speech (4125 tweets-per-minute).

Times shown are in Eastern Daylight Time (UTC -4 due to Daylight Savings Time). Stats in this report cover Day 4 (August 3oth). Click the images to view full-size.

Day Total

Tweets : 606,074

Users : 213, 882

Mentions by the minute

This graph shows tweet levels (mentions) per minute, from 6 PM – 12 AM on August 30th. Significant peaks are labeled on the graph. The peak times along with the main topic being tweeted about are listed below:

Format [Minute = Main speaker/topic being tweeted about (x users, y mentions)]

  • 19:55 PM = Newt & Callista Gingrich (734 users, 775 mentions)
  • 20:15 PM = Jeb Bush (915 users, 968 mentions)
  • 21:06 PM = Tom Stemberg (817 users, 860 mentions)
  • 21:35 PM = US Olympians (1035 users, 1083 mentions)
  • 22:03 PM = Clint Eastwood (2395 users, 2504 mentions)
  • 22:15 PM = End of Clint Eastwood’s speech (3749 users, 3954 mentions)
  • 22:21 PM = Marco Rubio (3008 users, 3189 mentions)
  • 22:36 PM = Mitt Romney gets on stage (3152 users, 3276 mentions)
  • 22:45 PM = Mitt Romney (3678 users, 3852 mentions)
  • 23:00 PM = Mitt Romney (3604 users, 3785 mentions)
  • 23:15 PM = Buzz after end of Mitt Romney’s speech; Balloon drop (3962 users, 4125 mentions)

Mentions by the hour

 

This graph shows tweet levels (mentions) and users tweeting per hour, from 00:00 – 23:59 on August 30th. The gap between the users and mentions indicates how hot the tweeting activity was. Mentions peaked at 185,165 tweets-per-hour at 10 PM.

The data is shown in the table below:

Hour Users Mentions
0 9279 14827
1 4661 7318
2 2665 4490
3 1892 3325
4 1217 2047
5 1190 2098
6 1857 2966
7 3042 4873
8 4441 6997
9 6235 9422
10 6714 10615
11 7223 10857
12 7636 11438
13 7432 11031
14 8217 12109
15 7916 11611
16 9086 13402
17 8247 12191
18 8773 13268
19 12335 21500
20 21264 42676
21 25962 50997
22 79982 185165
23 78043 140851

 

Location of Tweeple

This map shows where geo-located tweets on the convention were coming from in the United States. Each blue dot represents one tweet. Unlike previous days we couldn’t show English/Non-English tweets. This is because the Streaming API does not include Twitter’s auto-detected language. Our own language detection system is optimised for distinguishing English from Malay/Indonesian, so it is not appropriate to apply to this set of tweets.

Differences from yesterday are a lot more tweets are visible on the map, expanding on existing clusters.

Popularity of Searchterms

The list below is a breakdown of how many users wrote tweets containing each searchterm,  from 00:00 – 23:59 August 30th. It is ordered by the number of users.

  • #RNC = 123032 users, 299770 mentions
  • #GOP2012 = 69412 users, 178614 mentions
  • #RNC2012 = 53265 users, 127807 mentions
  • @MittRomney = 49836 users, 80497 mentions
  • #RomneyRyan2012 = 39154 users, 62978 mentions
  • @PaulRyanVP = 20622 users, 31021 mentions
  • #tcot = 16315 users, 58920 mentions
  • @GOPConvention = 3185 users, 5233 mentions
  • #WeBuiltIt = 1308 users, 1690 mentions

Its interesting that #RNC was more widely used than #GOP2012, the official hashtag for the convention. Mentions of @GOPConvention and #WeBuiltIt continued to remain low as in the previous days. We missed out on tracking #BelieveInAmerica.

Popular tweets

The most retweeted (RT) tweets of the day are listed below, in order. We count the number of users who RT, not the number of times. This is to reduce the impact of spammers. RTs shown only cover retweets made on August 30th. We will recalculate the popular tweets for the whole convention once its over.

1. Mitt Romney, 3212 RTs

2. Mitt Romney, 2811 RTs

3. Paul Ryan, 2114 RTs

4. Mitt Romney, 1822 RTs

5. Mitt Romney, 1739 RTs

6. Marco Rubio, 1548 RTs

Note

We collected 606, 074 tweets from 213,882 users. Our system was modified to collect tweets from both the Streaming API and Search API. This helped avoid any dips in the graph or ‘ceiling’ issues like in Day 3.  Twitter wrote a blog post giving the #GOP2012 stats as 2 million, with the peak at 14289 tweets per minute at the end of Mitt Romney’s speech. Our peak was 4125 tweets per minute. Their second peak was at 11.09 PM EST with 13267 tweets-per-minute. Our stats for that time show 3097 tweets-per-minute.

Using our combined API approach, we seem to be getting about 20-28% of the real total. But without more details on what searchterms Twitter is using, or similar graphs to ours, we can’t be sure. 95782 tweets about the convention came exclusively from Twitter Search, and were not obtained using the Streaming API. So for the sake of completeness, both APIs need to be used for major events.

Previous Reports

Day 1 & 2 (August 28th)

Day 3 (August 29th)

Written by politweet

September 5, 2012 at 6:25 pm

Posted in Analyses, Social Media

Tagged with , , , , , , , , , , , ,

#GOP2012 Twitter Report Day 3

#GOP2012 is the official hashtag of the Republican National Convention currently being held at Tampa Bay, Florida from August 27th – August 30th. During this period we tracked mentions of #GOP2012, #RNC, #RNC2012, #RomneyRyan2012, #tcot, @GOPConvention,  @MittRomney and @PaulRyanVP on Twitter. Starting Day 3 we also tracked #WeBuiltIt.

The highest levels of interest were for Condoleezza Rice (1232 tweets-per-minute), Susana Martinez (1353 tweets-per-minute) and Paul Ryan (1405 tweets-per-minute).

Times shown are in Eastern Daylight Time (UTC -4 due to Daylight Savings Time). Stats in this report cover Day 3 (August 29th). Click the images to view full-size.

Day Total

Tweets : 322,162

Users : 121, 009

Mentions by the minute

This graph shows tweet levels (mentions) per minute, from 6 PM – 12 AM on August 29th. Significant peaks are labeled on the graph. The peak times along with the main topic being tweeted about are listed below:

Format [Minute = Main speaker/topic being tweeted about (x users, y mentions)]

  • 20:14 PM = John McCain (692 users, 718 mentions)
  • 20:33 PM = Pam Bondi and Sam Olens (578 users,599 mentions)
  • 21:09 PM = Rob Portman (589 users, 620 mentions)
  • 21:34 PM = Tim Pawlenty (1023 users, 1090 mentions)
  • 21:54 PM = Mike Huckabee (979 users, 1014 mentions)
  • 21:57 PM = Condoleezza Rice (1023 users, 1059 mentions)
  • 22:01 PM = Condoleezza Rice (1051 users, 1099 mentions)
  • 22:07 PM = Condoleezza Rice (1189 users, 1232 mentions)
  • 22:17 PM = Susana Martinez and post-speech praise for Condoleezza Rice (1307 users, 1364 mentions)
  • 22:25 PM = Susana Martinez (1282 users, 1353 mentions)
  • 22:35 PM = Paul Ryan (1223 users, 1269 mentions)
  • 22:45 PM = Paul Ryan (1230 users, 1306 mentions)
  • 22:59 PM = Paul Ryan (1380 users, 1405 mentions)
  • 23:07 PM = Post-speech commentary (1357 users, 1423 mentions)

Our system had trouble keeping up with the tweet volume starting from the time Condoleezza Rice spoke. This is why the graph has many dips from that point on.

Mentions by the hour

This graph shows tweet levels (mentions) and users tweeting per hour, from 00:00 – 23:59 on August 29th. The gap between the users and mentions indicates how hot the tweeting activity was. The almost horizontal levels of tweets is an indication of a ‘ceiling’ that our system hit, because we used the Twitter Search API instead of the Streaming API. A discussion of that can be found in this blog post.

The data is shown in the table below:

Hour Users Mentions
0 9393 14485
1 4428 6715
2 2430 3821
3 1579 2343
4 1129 1802
5 1088 1733
6 1816 2623
7 2838 4086
8 3861 5670
9 6015 9035
10 6461 9700
11 6375 9893
12 6563 9855
13 6296 9643
14 6154 9218
15 6451 9440
16 6126 9167
17 6232 9056
18 6219 9407
19 10516 19245
20 14460 27037
21 19868 39471
22 28113 52754
23 28574 45963

Location of Tweeple

This map shows where geo-located tweets on the convention were coming from in the United States. Blue tweets are English, red tweets are non-English. Differences from yesterday are more tweets from Vancouver and more tweets distributed across America,  noticeably from Missouri until the east coastline.

Popularity of Searchterms

The list below is a breakdown of how many users wrote tweets containing each searchterm,  from 00:00 – 23:59 August 29th. It is ordered by the number of users.

  • #GOP2012 = 42049 users, 89308 mentions
  • #RNC = 45455 users, 80869 mentions
  • #RNC2012 = 32022 users, 65012 mentions
  • @MittRomney = 24588 users, 36093 mentions
  • #tcot = 15304 users, 55481 mentions
  • @PaulRyanVP = 14898 users, 21945 mentions
  • #RomneyRyan2012 = 10618 users, 17055 mentions
  • @GOPConvention = 3350 users, 5482 mentions
  • #WeBuiltIt = 2272 users, 2949 mentions

Compared to yesterday, the relative ordering remains the same except for mentions of @PaulRyanVP, which moved up a place. The new hashtag we tracked, #WeBuiltIt, had relatively few mentions.

Popular tweets

The most retweeted (RT) tweets of the day are listed below, in order. We count the number of users who RT, not the number of times. This is to reduce the impact of spammers. RTs shown only cover retweets made on August 29th. We will recalculate the popular tweets for the whole convention once its over.

1. Paul Ryan, 1289 RTs

2. Paul Ryan, 1167 RTs

3. Mitt Romney, 838 RTs

4. Mitt Romney, 831 RTs

5. Paul Ryan, 828 RTs

Note

We collected 322, 162 tweets from 121,009 users. Politweet uses Twitter’s Search API to track tweets for two reasons – to take advantage of Twitter’s spam-filtering; and because the Search API is closer to the end-user experience. Twitter wrote a blog post giving the #GOP2012 stats as over 2 million, with the peak at 6669 tweets per minute during Paul Ryan’s speech. Our peak was 1405 tweets per minute. Given this big discrepancy, for Day 4 of the convention we will be using both the Search API and Streaming API.

Written by politweet

August 31, 2012 at 6:07 am

Posted in Analyses, Social Media

Tagged with , , , , , , , , , , , ,

#GOP2012 Twitter Report Day 1 & 2

#GOP2012 is the official hashtag of the Republican National Convention currently being held at Tampa Bay, Florida from August 27th – August 30th. During this period we tracked mentions of #GOP2012, #RNC, #RNC2012, #RomneyRyan2012, #tcot, @GOPConvention,  @MittRomney and @PaulRyanVP on Twitter.

The highest levels of interest were for Ann Romney (1422 tweets-per-minute) and Chris Christie (1491 tweets-per-minute).

Most of Day 1 events were postponed due to weather conditions related to Hurricane Isaac, and schedule was merged into Day 2. Stats shown start from Day 2 (August 28th 2012). Times shown are in Eastern Daylight Time (UTC -4 due to Daylight Savings Time). Click the images to view full-size.

Day Total

Tweets : 287,226

Users : 103, 932

Mentions by the minute

This graph shows tweet levels (mentions) per minute, from 1 PM – 12 AM on August 28th. Significant peaks are labeled on the graph. The peak times along with the main topic being tweeted about are listed below:

Format [Minute = Main speaker/topic being tweeted about (x users, y mentions)]

  • 2:01 PM = GOP 2012 starts (200 users, 215 mentions)
  • 5:43 PM = Mitt Romney wins the nomination (600 users, 640 mentions)
  • 7:46 PM = Mia Love and Janine Turner (428 users, 444 mentions)
  • 7:47 PM = Mia Love and Janine Turner (487 users, 517 mentions)
  • 8:33 PM = John Kasich (675 users, 695 mentions)
  • 8:59 PM = Gov. Scott Walker (665 users, 702 mentions)
  • 9:33 PM = Rick Santorum (1029 users, 1076 mentions)
  • 9:35 PM = Rick Santorum(1054 users, 1093 mentions)
  • 10:17 PM = Ann Romney (1360 users, 1422 mentions)
  • 10:41 PM = Chris Christie (1427 users, 1484 mentions)
  • 10:50 PM = Chris Christie (teacher’s unions) (1230 users, 1262 mentions)
  • 11:00 PM = Chris Christie (asking everyone to stand up for Mitt Romney, also praise for Chris Christie’s speech) (1466 users, 1491 mentions)
  • 11:20 PM = Post-convention commentary (916 users, 961 mentions)

Mentions by the hour

This graph shows tweet levels (mentions) and users tweeting per hour, from 00:00 – 23:59 on August 28th. The gap between the users and mentions indicates how hot the tweeting activity was. The data is shown in the table below:

Hour Users Mentions
0 1032 1380
1 645 903
2 432 615
3 320 487
4 246 410
5 265 384
6 388 549
7 889 1239
8 1334 1871
9 2291 3060
10 2956 4130
11 3044 4084
12 4509 6514
13 6708 10594
14 6846 11374
15 7636 12649
16 8553 14825
17 11219 20529
18 9939 16099
19 10621 18871
20 15748 29539
21 20789 42260
22 26218 48011
23 24096 36849

Location of Tweeple

This map shows where geo-located tweets on the convention were coming from in the United States. Blue tweets are English, red tweets are non-English.

Popularity of Searchterms

The list below is a breakdown of how many users wrote tweets containing each searchterm,  from 00:00 – 23:59 August 28th. It is ordered by the number of users.

  • #GOP2012 = 39644 users, 92329 mentions
  • #RNC = 38240 users, 71349 mentions
  • #RNC2012 = 27557 users, 57593 mentions
  • @MittRomney = 24638 users, 39126 mentions
  • #tcot = 10625 users, 34678 mentions
  • #RomneyRyan2012 = 8030 users, 14230 mentions
  • @PaulRyanVP = 5230 users, 6750 mentions
  • @GOPConvention = 4548 users, 7839 mentions

Popular tweets

The most retweeted (RT) tweets of the day are listed below, in order. We count the number of users who RT, not the number of times. This is to reduce the impact of spammers. RTs shown only cover retweets made on August 28th.

1. Sarah Silverman, 1514 RTs

2. Mitt Romney, 1011 RTs

3. Ann Romney, 952 RTs

4. Mitt Romney, 877 RTs

5. Paul Ryan, 780 RTs

Note

At least 287,226 tweets about the GOP convention were tweeted by 103,932 users. Politweet uses Twitter’s Search API to track tweets for two reasons – to take advantage of Twitter’s spam-filtering; and because the Search API is closer to the end-user experience. However this means some genuine users are sometimes filtered out and their tweets are not obtained. Sometimes live tweets are missing from search results and only appear later, which causes them to be missed by our search.

For example despite the #RNC tag, Sarah Silverman’s original tweet was not found in our data, but all the retweets were. This is not to imply that Sarah Silverman was blocked, but a little missing data is just the nature of the Twitter search engine during live events. Journalists should note that the real totals are actually slightly higher.

Chris Christie delivered the highest number of tweets. For an idea of how big the crowd was while he was speaking, here is a screen capture from Youtube:

Update #1 (31st August 2012): Stats on this page are not ‘slightly lower’ as reported, but significantly lower. We caught on to the size of the discrepancy after Twitter wrote a blog post about the event. Explanation on the reason for this discrepancy is in this blog post. However the peaks on the graph are still relevant, and do indicate which speakers were most popular.

Written by politweet

August 29, 2012 at 10:13 pm

Posted in Analyses, Social Media

Tagged with , , , , , , , , , , , , ,

Popular phrases related to Hari Raya (Eid Ul-Fitr / Idul Fitri)

During the Raya break (August 17 – 23 2012) Politweet collected tweets containing Raya-related terms. These terms were decided based on expected terms and what trended in Malaysia during that period.

To try and focus more on Malaysia, Indonesian-majority terms such as ‘Maaf Lahir’ and ‘Idul Fitri’ were not tracked. However it was later discovered that ‘Idul Fitri’ is used by Malaysians as well.

A total of 1,653,999 tweets by 718,200 users were collected. The chart displays the top phrases among those tracked.The searchterms tracked were:

Selamat Hari Raya
Eid Mubarak
Happy Eid
Maaf Zahir
#SelamatHariRayaTo
Salam Aidilfitri
#SalamAidilfitri
Ied Mubarak
#HappyEid
#HappyEidTo
#TeringinBerayaBersama
#Raya2012

Terms that trended worldwide during this period

  1. Eid (730 mins, not tracked because too generic)
  2. Happy Ied Mubarak (370 mins, counted under Ied Mubarak above)
  3. Eid Mubarak (330 mins)
  4. #SelamatHariRayaTo (260 mins)
  5. Happy Eid Mubarak (150 mins)
  6. Happy Eid (20 mins)
  7. Selamat Hari Raya (10 mins)

Terms that trended in Malaysia during this period

  1. Maaf Zahir & Batin (2930 mins, counted under Maaf Zahir above)
  2. #SelamatHariRayaTo (1620 mins)
  3. Happy Eid (1520 mins)
  4. Happy Eid Mubarak (1520 mins, counted under Happy Eid)
  5. Salam Aidilfitri (1250 mins)
  6. #SalamAidilfitri (680 mins)
  7. Eid (530 mins)
  8. Selamat Hari Raya (220 mins)
  9. Eid Mubarak (180 mins)
  10. #HappyEid (50 mins)
  11. #Raya2012 (40 mins)
  12. #HappyEidTo (30 mins)
  13. #TeringinBerayaBersama (20 mins)

Some phrases such as ‘Selamat Hari Raya’ are mainly used by Malaysians and Indonesians, while Eid, Ied and similar terms are used globally. There is no practical way to tell how many users are Malaysian.

Facts and observations

Out of the total tweets, 836,785 (50.59%) were in Malay/Indonesian and 469,708 (28.40%) were in English. The number of Malay tweets is likely even higher (est. 100-200K) as Malay tends to be miscategorised as other languages.

Out of the total users, 371,101 (51.67%) tweeted in Malay/Indonesian and 320,527 (44.63%) tweeted in English. There is some overlap between the 2 groups.

62% of Eid Mubarak users tweeted in English.

91% of Selamat Hari Raya users tweeted in Malay/Indonesian.

Both phrases peaked at the same period, August 19th 5 AM – 7 AM (GMT+8).

Eid Mubarak tweet levels remained consistent until the start of August 21st. This is because the term was used globally at all hours.

Selamat Hari Raya tweet levels were cyclical – high in the morning and evening and very low at night. This is consistent with Malaysian tweeting patterns.

63,402 users (8.8%) follow Malaysian politicians. Its smaller than expected because 1.03 million users follow Malaysian politicians.

Additional charts

The Most Retweeted ‘Selamat Hari Raya’ Tweets (tweet, RTs)

  • “RT @LFC: Selamat Hari Raya Idul Fitri 1433H bagi semua pendukung The Reds di Indonesia yang merayakan. Mohon maaf lahir bathin, dari kami di @LFC”, 682
  • “RT @MotivasiCintaku: untuk para Jomblo Selamat Hari Raya Idul Fitri 1433 H. Lebaran itu yg penting bukan pakaian baru tapi STATUS BARU! (⌣̩_⌣ ) #MotivasiCintaku”, 367
  • “RT @DoaIndah: “Taqobbalallaahu minna wa minkum, Minal A’idin Wal Faidzin Kullu aamin wa antum bi khoir. Selamat Hari Raya Idul Fitri 1433H”.”, 361
  • “RT @yunamusic: Eid Mubarak everybody, Selamat Hari Raya, maaf zahir dan batin, have a wonderful celebration.”, 295
  • “RT @AbahYou_Tweet: *dapat kad raya* *baca* *dari crush* “selamat hari raya btw saya dah lama suka awak” *sesak nafas* *pengsan* *koma* *mati*”, 292
  • “RT @DoaIndah: “Taqobbalallaahu minna wa minkum, Minal A’idin Wal Faidzin Kullu aamin wa antum bi khoir. Selamat Hari Raya Idul Fitri 1433H””, 290
  • “RT @mrezanugrah: Selamat Hari Raya Idul Fitri 1 Syawal 1433 Hijriyah. Minal aidin wal faidzin ya. Mohon maaf lahir dan batin, kita semua kembali ke fitrah..”, 275
  • “RT @KamusCewek: Selamat Hari Raya Idul Fitri 1433H, mohon maaf lahir & batin ;)”, 255
  • “RT @officialJKT48: Seluruh staff JKT48 mengucapkan selamat Hari Raya Idul Fitri 1433 H. Minal Aidin Wal Faidzin, mohon maaf lahir dan batin :)”, 250
  • “RT @bepe20: Saya sekeluarga mengucapkan: Selamat hari raya Idul Fitri 1433 H, Mohon maaf lahir dan bathin..”, 220

The Most Retweeted ‘Eid Mubarak’ Tweets’ (tweet, RTs)

  • “RT @zaynmalik: Eid Mubarak, thankyou to everyone who wished me a happy eid. Love you all 🙂 x”, 35752
  • “RT @SamNasri19: Aid mabrouk a tous les musulman, Eid Mubarak to all the muslims”, 2833
  • “RT @sneijder101010: Eid Mubarak to all my Muslim followers…. Have a great Sunday everyone….”, 2123
  • “RT @LupeFiasco: Eid Mubarak from Los Angeles…Inshallah Allah SWT has accepted our fasts and prayers this Ramadan!”, 1636
  • “RT @SHAQ: Dear Friends, I want to take this special time of the year to wish you and your families a Eid Mubarak during this memorable time of the yr”, 1343
  • “RT @Milanello: Eid Mubarak to all Muslims around the world!”, 1051
  • “RT @thegame: Eid Mubarak !”, 899
  • “RT @iamsrk: Eid Mubarak to everyone.May Allah fill your life with happiness. Look forward to reading the Takbirat in an open space in Ladakh.Insha Allah”, 897
  • “RT @TheRealAC3: Happy eid mubarak 2 all my muslim followers!! #bless”, 847
  • “RT @jaysean: Eid Mubarak to all my muslim fans out there!! Love to u all!”, 807

Written by politweet

August 24, 2012 at 4:54 am

Posted in Analyses, Social Media

Tagged with , , , , ,

Top Mobile Platforms (as of May 2012)

 

These graphs chart the monthly number of tweets and users tweeting using a Blackberry, iPhone, iPad and Android device from April 2010 – May 2012.

This is based on mentions of Malaysian politicians on Twitter, and does not include tweets on socio-economic and political topics (e.g. Bersih). Because of that these statistics are reflective of the Malaysian population.

Blackberry users have continued to dominate in terms of tweet volume and user base. Since March 2012 Apple users have been steadily increasing, with iPad users overtaking Android users in May 2012.

Written by politweet

June 15, 2012 at 4:15 am

Posted in Social Media

Tagged with , , ,

Stats on #DebatPTPTN

#DebatPTPTN was a debate on the PTPTN student loan scheme between @RafiziRamli (PKR) and @KhairyKJ (UMNO). It was organised by @SinarOnline. Statistics shown are from 9pm – 3am.

A total of 6,693 users wrote 17,611 tweets about the event. It peaked between 11pm-12am, with 7,513 mentions by 3,788 users. In total, @KhairyKJ was mentioned 6,569 times by 3,241 users and @RafiziRamli was mentioned 4,013 times by 1,936 users.

Interest can be roughly gauged by dividing mentions by users. @RafiziRamli had a slightly higher level of interest compared to @KhairyKJ, and gained 1,555 followers. @KhairyKJ gained 1,029 followers.Sentiment is hard to gauge due to supporters from both sides giving biased reporting. A brief survey of the tweets showed that:1. Users accused both debaters of being too focused on politics and accusations, and going off-topic
2. People were more focused on Khairy and Rafizi’s ability to answer questions rather than the answers themselves
3. There was not much discussion or comments on the ideas being discussed

Twitter Trends

‘PTPTN’ trended at 6th place at 11pm for 10 minutes.

In KL, ‘Rafizi’ trended at 6th place at 11.40pm, until reaching 3rd place at 2am. Rafizi continued to trend until 8.30am, by which time he had dropped to 4th place.

In Malaysia, ‘Rafizi’ trended at 10th place at 11pm, reaching 2nd place at 1.40am. He stopped trending at 8.30am, ending at 3rd place.

No other Malaysian or KL trends were relevant to this debate.

Written by politweet

May 23, 2012 at 4:08 am

Posted in Analyses

Tagged with , , , , , , , , ,

The Most Retweeted Political Parties (April 2010 – March 2012)

Retweets (RT) are tweets from others that you repeat to your followers. The charts below show the number of users retweeting politicians’  tweets and the number of retweets sent, by coalition and party. Overall BN was retweeted (RT’d) less than PR, but BN was RT’d by more users.

Coalition leaders took 2nd and 3rd place among the most RT’d – @NajibRazak with 16,983 RT’s and @AnwarIbrahim with 13,750 RTs.

BN has an advantage in @KhairyKJ, who took 1st place with 21,162 RTs. His closest counterpart in PR is @LimKitSiang with 5,949 RTs.

When ranked by the number of users retweeting, the order changes slightly. 1st is @NajibRazak who was retweeted by 10,337 users; 2nd is @KhairyKJ with 8,168 users; and 3rd is @AnwarIbrahim with 5,423 users.

Only raw retweets (no comments added) were used to calculate these statistics. By retweeting a politician, a user helps to advertise a politician’s message to his/her followers. This can lead to an increase in follower count for a politician. In theory a politician who is popular will often get his/her tweets RT’d.

Ranking by users and retweets are both important for analysts. Discrepancies in ranking result in 2 situations, with these possible causes:

1)High in user rank; Low in retweet rank => cause: active follower-base; infrequent tweeting; tweets not worth retweeting

2)Low in user rank; High in retweet rank => cause: users may be retweeting in a closed or semi-closed network, and therefore not helping to grow the user-base that retweets; frequent tweeting; retweetable tweets

A large discrepancy may also indicate involvement in a hot issue. For example, Dr.Rais Yatim is no.19 when ranked by users, but no.27 when ranked by RTs. This is because his statements on the Erykah Badu concert ban were widely retweeted.

A table of Top 30 politicians for both ranking orders is included at the end of this report.

As of end of February 2012, PR had 102,134 active followers* whereas BN had 226,844 active followers. Despite the high number of retweets PR is lagging behind BN in follower growth. This could indicate a problem of ‘preaching to the converted’ or a problem with the message – something to be explored in future research.

Ahmed Kamal
3rd April 2012

*active followers = followers that had tweeted or followed/unfollowed other users in the last 3 months

============================================
Summary of data used:

Pakatan Rakyat
————–
Total users 14598
Total retweets 81622

Barisan Nasional
—————-
Total users 20833
Total retweets 65693

PR Component Parties (name, users, retweets)
——————————————–

PKR 10520 47246
DAP 4970 25053
PAS 3201 9323

BN Component Parties (name, users, retweets)
——————————————–

UMNO 20176 59763
MCA 1235 3416
MIC 675 2480

Top 30 Most RT’d politicians, by retweets (@username, users, retweets)
———————————–
1. khairykj 8168 21162
2. najibrazak 10337 16983
3. anwaribrahim 5423 13750
4. limkitsiang 1701 5949
5. mpkotabelud 1228 4368
6. mukhrizmahathir 2393 4216
7. tianchua 1430 4092
8. n_izzah 2192 4075
9. hishammuddinh2o 1256 3676
10. tonypua 1405 3666
11. hannahyeoh 1663 3293
12. niknazmi 1257 3121
13. saifuddinabd 1232 2948
14. nikabdulaziz 1453 2821
15. elizabethwong 1088 2663
16. ngakorming 539 2492
17. khalid_ibrahim 1057 2299
18. azminali 919 2257
19. pkamalanathan 609 2149
20. saifnasution 720 1944
21. drdzul 739 1836
22. syedhusinali 914 1722
23. shamsuliskandar 576 1716
24. weekasiongmp 643 1538
25. fuziah99 464 1466
26. mbnizar 688 1389
27. drraisyatim 780 1349
28. simtzetzin 516 1199
29. tantawi100 639 1160
30. cmlimguaneng 560 1128

Top 30 Most RT’d politicians, by users (@username, users, retweets)
———————————–
1. najibrazak 10337 16983
2. khairykj 8168 21162
3. anwaribrahim 5423 13750
4. mukhrizmahathir 2393 4216
5. n_izzah 2192 4075
6. limkitsiang 1701 5949
7. hannahyeoh 1663 3293
8. nikabdulaziz 1453 2821
9. tianchua 1430 4092
10. tonypua 1405 3666
11. niknazmi 1257 3121
12. hishammuddinh2o 1256 3676
13. saifuddinabd 1232 2948
14. mpkotabelud 1228 4368
15. elizabethwong 1088 2663
16. khalid_ibrahim 1057 2299
17. azminali 919 2257
18. syedhusinali 914 1722
19. drraisyatim 780 1349
20. drdzul 739 1836
21. saifnasution 720 1944
22. mbnizar 688 1389
23. weekasiongmp 643 1538
24. tantawi100 639 1160
25. pkamalanathan 609 2149
26. shamsuliskandar 576 1716
27. cmlimguaneng 560 1128
28. ngakorming 539 2492
29. sivarasarasiah 536 1048
30. simtzetzin 516 1199

Written by politweet

April 3, 2012 at 3:50 am

Posted in Analyses, Social Media

Tagged with , , , ,

Location of politicians’ active followers (as of Feb 2012)

Politweet ran two censuses of politicians’ followers on Twitter, on December 4th 2011 and February 25th 2012. Based on the results it became clear that followers fell into 3 categories:

1) Suspended – Twitter includes suspended users in the list of followers. It is not known whether these suspended users eventually get deleted, our data from 2009 shows that a follower count constantly increases

2) Dummy – these are users who have 0 tweets and 0 followers. They may be automated bots or accounts created for the sole purpose of passive consumption of tweets

3) Inactive – these are users who have shown no change in their activity stats (tweets, friend count) in the last 3 months. They may have stopped using Twitter entirely, or switched to passive consumption
4) Active – users who have tweeted or followed/unfollowed others in the last 3 monthsThese charts are based on active followers because they are the ones most likely to spread information within the Twitter network.

The country of residence for each user was derived from their stated location and in some cases, language and time zone. Time zone was used minimally because most users leave it at the default (Alaska) and many users state the wrong time-zone. For example, we found 2500 users who stated their time zone as Beijing. Based on the names, location and latest tweets from a sample, we found that most of them are actually Malaysians. So we had to avoid using time zone to determine location. The country could not be determined for 413,271 users.

For the most part, both political coalitions have minor percentage differences for their followers. Barisan Nasional (BN) active follower base is 2.22 times larger than Pakatan Rakyat’s (PR), so for most countries BN has more followers. However when it comes to Indonesia, PR has attracted more followers than BN. Anwar Ibrahim is the main contributing factor as 2,381 or 40.96% of PR’s Indonesian followers only follow him.

That is the only point worth noting for now, but as we run more censuses we should be able to determine growth trends among followers.

Ahmed Kamal
29th March 2012

=====================
Summary of the data, from census done between February 25th – March 1st 2012

There are 636,949 followers in the total population. 274,328 (43.07%) are active.
BN has 512,433 followers. 226,844 (44.27%) are active.
PR has 239,767 followers. 102,134 (42.60%) are active.

*numbers shown are count of users

Barisan Nasional (BN) followers
——————————-
MY 121055
Unknown 97924
ID 1386
GB 1310
AU 858
DE 662
SG 661
KR 609
US 573
JP 294
IN 210
EG 209
TH 154
BN 147
RU 115
CA 94
TR 76
FR 71
AE 69
NZ 64
IE 56
PH 52
BD 47
CN 46
PK 43
HK 33
NL 26

Pakatan Rakyat (PR) followers
—————————–

MY 52881
Unknown 39336
ID 5812
GB 891
AU 679
SG 487
US 368
EG 251
KR 170
JP 166
IN 130
TH 111
PH 106
DE 103
PK 78
TR 78
RU 75
BD 63
CA 55
HK 49
BN 46
NZ 45
AE 39
CN 35
IE 32
FR 25
NL 23

Written by politweet

March 29, 2012 at 3:44 am

Posted in Analyses, Social Media

Tagged with , , , , ,

Language trends in Malaysia’s political discourse (as of 25th March)

The language of political discourse on Twitter has always been a mix of English and Malay, however since August 2011 Malay has overtaken English as the dominant language. This graph shows the number of Twitter users who mention politicians each month based on the language used.

The data used for this graph came from 1.5 million tweets that Politweet analysed to determine the language. This includes retweets, so the results are not based on original content by each user.

Malay tweets were particularly hard to identify due to use of slang and intentional removal of vowels, so many tweets were mismatched against other languages especially Portuguese and Spanish.It was found that from the population of 109,268 users*:

a) 105,424 (96.48%) speak English or Malay
b) 68,350 (62.72%) speak English and 69,042 (63.19%) speak Malay.
c) 37,074 (33.93%) exclusively speak Malay;
d) 36,382 (33.30%) exclusively speak English;
e) 31,968 (29.26%) exclusively speak a mix of English and Malay.
f) 2,593 (2.37%) speak Chinese
g) 776 (0.71%) exclusively speak Chinese
h) 1,778 (1.63%) of the Chinese speaking users tweeted in English or Malay.

*numbers in parentheses represent percentages of the population

The remaining 3,389 users (3.10%) in the population tweeted in other languages or could not be identified. Chinese speakers were not included in the graph due to their low numbers and the fact that many also tweeted in English or Malay.

Please note that the graph is not based on exclusive English/Malay-speaking users, so users who speak in both languages count towards both totals.

Ahmed Kamal
27th March 2012

=====================
Summary of the data, from 4th April 2010 – March 25th 2012

Format: Month/Year Users

Malay speakers
————–
4/10 893
5/10 940
6/10 1121
7/10 1231
8/10 1431
9/10 1416
10/10 2263
11/10 2170
12/10 3284
1/11 4161
2/11 3153
3/11 4466
4/11 5467
5/11 6828
6/11 5002
7/11 6006
8/11 7423
9/11 9259
10/11 11294
11/11 12930
12/11 12336
1/12 14938
2/12 13414
3/12 14930

English speakers
—————-
4/10 2110
5/10 2173
6/10 2583
7/10 2466
8/10 2469
9/10 2445
10/10 3482
11/10 3293
12/10 5259
1/11 5120
2/11 4367
3/11 6298
4/11 8180
5/11 8354
6/11 6144
7/11 7983
8/11 6862
9/11 9151
10/11 11037
11/11 10477
12/11 10398
1/12 14612
2/12 13180
3/12 11876

Chinese speakers
—————-
4/10 17
5/10 18
6/10 27
7/10 16
8/10 24
9/10 33
10/10 48
11/10 40
12/10 47
1/11 95
2/11 102
3/11 95
4/11 148
5/11 241
6/11 121
7/11 89
8/11 131
9/11 164
10/11 212
11/11 239
12/11 234
1/12 370
2/12 280
3/12 229

Total users (includes other languages)
————————————–
4/10 2427
5/10 2600
6/10 3057
7/10 3009
8/10 3256
9/10 3162
10/10 4602
11/10 4388
12/10 7033
1/11 7466
2/11 6247
3/11 8718
4/11 11209
5/11 12519
6/11 9208
7/11 11524
8/11 11822
9/11 15158
10/11 18465
11/11 19585
12/11 18821
1/12 24391
2/12 22080
3/12 22284

Written by politweet

March 27, 2012 at 3:31 am

Posted in Analyses, Social Media

Tagged with , , ,

Budget 2012 : The Possible Future of Public Debt

The public debt is the total amount owed by the Federal Government, both domestic and external. Since 1997 the Federal Government debt (public debt) has steadily increased. The latest figure for June 2011 places the debt at RM 437 billion.

Not only has the total debt been increasing, but the rate of growth has for the most part remained high. It crossed RM100 billion in 1998. It crossed 200 billion in 2004. It crossed 300 billion in 2008. It crossed 400 billion in 2010.

In other words, the time taken for the debt to grow has been:

100 billion to 200 billion = 6 years
200 billion to 300 billion = 4 years
300 billion to 400 billion = 2 years

That is an exponential growth rate. The average growth rate from 2005 – 2010 was 10.6%/year. If the current growth rate holds steady at 10%, the public debt will cross RM 1 trillion by 2020.

This chart shows the size of the public debt from 1995 – 2020, based on an estimated growth rate of 10% per year from June 2011 onwards. This serves as an illustration of the possible future of the public debt based on recent trends.

While the debt ceiling ensures this will not happen easily, this chart should make it clear that recent trends are not sustainable for the future. Economic reforms are needed to drive up revenue, lower expenditure or both.

Ahmed Kamal
19th October 2011

Update (20th October 2011)
The 1 Trillion figure is too far in the future to be proven valid or invalid, so there is no point in debating that. A 10% growth per year is a simplistic approach, but my intention is to get people to take notice and start asking the government and opposition how their economic policies will affect the debt.

If the public debt increases were largely due to development expenditure (true in most years) then it should ultimately be paid off by the revenue brought in by the development. But we have had larger increases in operating expenditure that have helped contribute to the debt. Our government has a tendency to increase operating expenditure hand-in-hand with revenue increases, if not through planned budget increases then via Supplementary Bills.

The most recent IMF forecast for 2016 estimates government revenue to be 293 billion, and the gross government debt to be 695 billion. This is lower than the 704 billion shown here. Nett debt would be smaller, lowest I can estimate based on IMF’s earlier figures is 670 billion. That is still higher than my 2015 estimate.

——
Data used in this graph:

Format : Year – Amount

Public Debt (RM million)

1995 – 91369
1996 – 89681
1997 – 89920
1998 – 103121
1999 – 112118
2000 – 125626
2001 – 145724
2002 – 164963
2003 – 188767
2004 – 216624
2005 – 228670
2006 – 242225
2007 – 266722
2008 – 306437
2009 – 362386
2010 – 407101
June 2011 – 437182
2012 – 480900.2
2013 – 528990.22
2014 – 581889.242
2015 – 640078.1662
2016 – 704085.9828
2017 – 774494.5811
2018 – 851944.0392
2019 – 937138.4431
2020 – 1030852.287

Written by politweet

October 19, 2011 at 1:22 am

Posted in Analyses

Tagged with , , ,

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