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The Most Followed Twitter Users in Malaysia (Oct 2017)

Since 2014 we have been building up a database of profiled Twitter users in Malaysia. We currently have over 630,000 profiled user accounts that are location-based. What this means is that we can analyse opinions and interests not just by state, but by area (e.g. cities, constituencies, campuses, malls, suburbs / taman). We have demonstrated the application of this database for opinion analysis (browse here) and by-elections (link). We are currently working on improving the level of detail for our profiles and are now sharing part of our research results with the public.

Using a sample of 24,677 users from our database, we collected their lists of Twitter ‘friends’ (user accounts that people follow). This resulted in a list of 2.07 million users. This list was then used to summarise the top 207,500 most-followed users by users in Malaysia.

The Top 10 most-followed Twitter users are below:

Rank @ScreenName Name Market Reach (%)
1 instagram Instagram 39.385
2 Khairykj Khairy Jamaluddin 35.665
3 9GAG 9GAG 32.577
4 Matluthfi90 Matluthfi90 27.459
5 yunamusic Yuna Zarai 25.064
6 501Awani Astro AWANI 23.982
7 NajibRazak Mohd Najib Tun Razak 23.625
8 waktuSolatKL Waktu Solat WP KL 22.653
9 SantapanMinda Santapan Minda 22.134
10 ustazharidrus Ust Azhar Idrus 21.818

Market reach is defined as the percentage of users in Malaysia who follow that Twitter user. Based on this list, Khairy Jamaluddin (MP for Rembau, Minister of Youth and Sports, UMNO Youth Leader) is both the most-followed person and most-followed Malaysian in the country. But his market reach is only 35.665% of users in Malaysia. This shows that no single user on Twitter ‘owns’ the Malaysian market. Because we are using profiled users, the possibility of fake followers (or phantoms, fake accounts etc.) is a non-issue.

The Top 10 users have a combined market reach of 82.25%. Most Twitter users in Malaysia have a market reach that would be considered small. But a small market reach does not mean that a tweet has no chance of going viral. Due to the high degree of connectivity between Twitter users plus the Twitter Search factor, there is always a chance for a tweet getting retweeted and spread throughout the network.

Using the data that we collected, we performed a network analysis on how the most-followed Twitter users are connected to each other based on their followers. For this analysis we used the top 4,704 users. This covers all user accounts followed by users in Malaysia with a minimum market reach of 0.61%.

Users that have a shared appeal (affinity) will have overlapping audiences, which is equal to strong connections if the overlap is high. For example, users that tweet primarily about football will draw interest from other people who like football.

Based on the network analysis we generated a map showing clusters of users with a strong affinity for each other. Based on where they are in the map, you can see the affinity that different popular users have with each other. Users with a greater market reach are shown in a larger font, coloured from a scale ranging from blue (least popular) to orange to red (most popular).

MsiaTopFollowed_2k_20perc_Oct2017C_24pxfont

The full-size version can be viewed at our Flickr page here.

At a glance you can see that the top users are close to each other where @Khairykj and @instagram are visible. As stated earlier the Top 10 users have a combined market reach of 82.25%. Despite the fact that these users don’t tweet about the same topics, their proximity to each other is due to their mass market appeal.

MsiaTopFollowed_TopUserCenter

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How Political Interest is Divided by Language on Facebook in Malaysia (July 2017)

1. Introduction

This document provides a measurement of the political party interests of Facebook users in Malaysia. This is based on public information collected from Facebook.

Some important notes to remember when interpreting Facebook figures:

  1. Total population refers to Facebook users aged 13 years and above.
  2. Potential voters refer to Facebook users aged 21 years and above.
  3. Youth refers to Facebook users aged 13 – 20 years.
  4. Gender breakdown figures do not always add up to the total. This may be due to Facebook users not sharing their gender, and also due to rounding errors in statistics provided by Facebook. State breakdown figures also do not add up to the total due to the same rounding errors.
  5. Detailed statistics on Putrajaya are not available due to the small number of users in the territory.
  6. Figures provided by Facebook are estimates. Some inaccuracies are to be expected, e.g. the sum of state totals not being equal to the national total.
  7. Facebook users residing in Malaysia are not necessarily Malaysian citizens.
  8. Interest in a topic is equal to the number of users expressing interest in a topic.
    1. To measure interest we used a combination of Facebook Interests (a collection of interests, activities, groups, pages, status updates and job history identified by a common term determined by Facebook e.g. ‘United Malays National Organization’) and specific Group and Page names (e.g. Friends of BN).
    2. These are used to collect the number of users interested in a given party/coalition/politician/group. For example, a user mentioning a party name in a status update; sharing a news link related to the party or sharing content from a party-affiliated page would count towards the total interest in that party
    3. Interest in a political party does not indicate support for the party, only awareness
    4. It is currently assumed that interest in PAS includes some interest in AMANAH as PAS leaders and members migrated to AMANAH
  9. Audience refers to the population of users that express interest in a topic. Unless indicated, the audiences used in this report are composed of potential voters (users in Malaysia aged 21 years and above).
  10. Based on our research to date, Pages that are of type ‘politician’ are not always included under related Facebook Topics. For example, not all ‘Tony Pua’ (MP, PJ Utara, DAP) Page likes are included under interest in ‘DAP’. However, because Facebook does not make Topic details available we cannot easily determine which politicians, if any, were included.
  11. Statistics on the Opposition primarily refer to component parties of the former Pakatan Rakyat – PKR, PAS and DAP. This includes the ‘Pakatan’ brand name.
  12. July 2017 statistics were collected during a 2-week period in July 2017. As such there may be some differences in totals for political parties when comparing different sections due to changes in collected statistics.
  13. Statistics on the 2017 1st Quarter electoral roll are estimates based on published changes to the 2016 gazetted roll. Ethnic breakdown for new voters are based on profiling methods that we developed and should be considered estimates.

2. List of Acronyms

The following table shows a list of acronyms used in this document.

Acronym Full name
PR Pakatan Rakyat
PH Pakatan Harapan
BN Barisan Nasional
UMNO United Malays National Organisation
GERAKAN Parti Gerakan Rakyat Malaysia (also known as PGRM)
MCA Malaysian Chinese Association
MIC Malaysian Indian Congress
PBB Parti Pesaka Bumiputra Bersatu Sarawak
PKR Parti Keadilan Rakyat
DAP Democratic Action Party
AMANAH Parti Amanah Negara
PAS Parti Islam Se-Malaysia
PPBM Parti Pribumi Bersatu Malaysia

3. An Overview of Malaysia’s Facebook User Population (July 2017)

3.1 Division by Age and Gender

There are currently 24 million Facebook users in Malaysia. 54.17% are men and 45.83% are women.

From this total, 19 million users are aged 21 years and above. 52.63% are men and 47.37% are women. These are the potential voters on Facebook.

The chart below shows the population distribution by age group. The largest segment of the population is aged between 21 – 30 years.

wp_langdivide_chart1

The table below shows the distribution of Facebook users by state, sorted by the total population:

State Total
(13+ yrs)
Male (%) Female (%) % of Malaysia
Perlis 40,000 52.50 47.50 0.17
Labuan 170,000 52.35 46.47 0.71
Kelantan 290,000 51.72 44.83 1.21
Terengganu 370,000 51.35 48.65 1.54
Negeri Sembilan 380,000 55.26 47.37 1.58
Melaka 390,000 53.85 46.15 1.63
Pahang 500,000 54.00 48.00 2.08
Kedah 610,000 54.10 47.54 2.54
Perak 880,000 51.14 48.86 3.67
Penang 990,000 51.52 48.48 4.13
Sabah 1,000,000 53.00 47.00 4.17
Sarawak 1,100,000 51.82 45.45 4.58
Johor 1,900,000 57.89 45.79 7.92
KL + Selangor 15,000,000 56.67 42.67 62.50

 

The table below shows the distribution of Facebook users by state aged 21 years and above.

State Total (>=21 yrs) Male (%) Female (%) % of Msia (>=21 yrs) % of State (>=21 yrs)
Perlis 33,000 51.52 48.48 0.17 82.50
Labuan 140,000 52.14 45.71 0.74 82.35
Kelantan 230,000 52.17 43.48 1.21 79.31
Terengganu 290,000 51.72 48.28 1.53 78.38
Negeri Sembilan 310,000 54.84 48.39 1.63 81.58
Melaka 320,000 53.13 46.88 1.68 82.05
Pahang 400,000 55.00 47.50 2.11 80.00
Kedah 490,000 53.06 46.94 2.58 80.33
Perak 710,000 50.70 47.89 3.74 80.68
Sabah 790,000 53.16 46.84 4.16 79.00
Penang 840,000 50.00 47.62 4.42 84.85
Sarawak 840,000 53.57 46.43 4.42 76.36
Johor 1,600,000 55.00 45.00 8.42 84.21
KL + Selangor 12,000,000 56.67 44.17 63.16 80.00

 

Based on the last column we can see that Sarawak, Terengganu, Kelantan and Sabah have the highest proportion of young users (below 21 years).

As of 2017 Quarter 1, an estimated 21.64% of registered voters reside in KL and Selangor. In the National Census 2010, 24.35% of Malaysia’s citizens and 24.11% of Malaysia’s total population reside in KL and Selangor.

However according to statistics from Facebook, 62.50% of Facebook users in Malaysia reside in KL and Selangor. This includes Malaysians and foreigners who live there. This is an increase from 50% in August 2016.

The heavy concentration of users in KL and Selangor means that trending content in Malaysia in terms of shares and likes might not reflect what the country is talking about. When it comes to the analysis of interest in local issues such as politics, it is therefore important to evaluate the interests of users in different states.

3.2 Division by Language

The chart below shows the number of potential voters by language used on Facebook, based on information they have shared with Facebook:

wp_langdivide_chart2

Hindi/Tamil = users who use Hindi or Tamil. Only 20 thousand users use both languages

If we added the totals together there would be 30 million users. Given that there are only 19 million Facebook users, there is an overlap between users from each group. Many users speak multiple languages.

93% of potential voters on Facebook use English, Malay or Chinese languages. Because of this high coverage, we were able to design a set of formulas to break up these users into smaller, identifiable groups based on different combinations of spoken languages. The population of users in these groups can then be estimated. The results of this analysis are in the table below:

Language Group Code % of Population (>=21 years) Description
Bilingual Malay + English BME 40.26 Users who speak Malay and English. May also speak other languages except Chinese.
English Only / English + Other languages EO 19.21 Users who speak English but do not speak Malay or Chinese. May also speak other languages.
Malay Only / Malay + Other languages MO 13.95 Users who speak Malay but do not speak English or Chinese. May also speak other languages.
Bilingual Chinese + English BCE 12.37 Users who speak both Chinese and English. May also speak other languages except Malay.
Other Languages Only OTH 7.11 Users who do not speak English, Malay or Chinese
Chinese Only / Chinese + Other languages CO 3.42 Users who speak Chinese but do not speak English or Malay. May also speak other languages.
Bilingual Malay + Chinese BMC 1.84 Users who speak both Malay and Chinese. May also speak other languages except English.
Trilingual Malay + English + Chinese TRI 1.84 Users who speak English, Malay and Chinese. May also speak other languages.

 

The proportion of each group is summarised in the chart below.

wp_langdivide_chart3

From the chart we can observe that:

  • The Bilingual Malay + English (BME) group is both the largest group of users and largest subset of Malay speakers in the country
  • Most Malay speakers on Facebook understand English
  • The Bilingual Chinese + English (BCE) group is the 4th largest group of users and largest subset of Chinese speakers in the country
  • Most Chinese speakers on Facebook understand English
  • A minority of users (3.68%, 700 thousand) speak combinations of Malay and Chinese

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Written by politweet

October 11, 2017 at 1:11 pm

Recent Trends in Political Party Interest on Facebook in Malaysia (Aug 2016)

1. Introduction

This document provides a measurement of the political party interests of Facebook users in Malaysia. This is based on public information collected from Facebook.

Some important notes to remember when interpreting Facebook figures:

  1. Total population refers to Facebook users aged 13 years and above.
  2. Potential voters refer to Facebook users aged 21 years and above.
  3. Youth refers to Facebook users aged 13 – 20 years.
  4. Gender breakdown figures do not add up to the total. This may be due to Facebook users not sharing their gender, and also due to rounding errors by Facebook when dealing with specific age groups. State breakdown figures also do not add up to the total, due to the same rounding errors.
  5. Figures provided by Facebook are estimates. Some inaccuracies are to be expected.
  6. Facebook users residing in Malaysia are not necessarily Malaysian citizens.
  7. Interest in a topic is equal to the number of users expressing interest in a topic.
    1. To measure interest we used a combination of Facebook Interests (a collection of interests, activities, groups, pages, status updates and job history identified by a common term determined by Facebook e.g. ‘United Malays National Organization’) and specific Group and Page names (e.g. Friends of BN).
    2. These are used to collect the number of users interested in a given party/coalition/politician/group. For example, a user mentioning a party name in a status update; sharing a news link related to the party or sharing content from a party-affiliated page would count towards the total interest in that party
    3. Interest in a political party does not indicate support for the party, only awareness
    4. It is assumed that interest in PAS includes interest in AMANAH as PAS leaders migrated to AMANAH
  8. Audience refers to the population of users that express interest in a topic.
  9. Based on our research to date, Pages that are of type ‘politician’ are not always included under related Facebook Topics. For example, not all ‘Tony Pua’ (MP, PJ Utara, DAP) Page likes are included under interest in ‘DAP’. However because Facebook does not make Topic details available we cannot easily determine which politicians, if any, were included.
  10. Statistics on the Opposition primarily refer to component parties of the former Pakatan Rakyat – PKR, PAS and DAP. Interest in PSM is included in total statistics for the Opposition, but is not listed separately due to its small audience.

 

2. Interest in Political Parties on Facebook

The following graph shows the partisanship of interest in political parties by Facebook users in Malaysia aged 21 years and above. Interest in PAS is assumed to include interest in AMANAH because Facebook has not made separate AMANAH figures available yet.

FBPartisanship_Aug15_Aug16

Out of 8.4 million users in Malaysia (aged 21 years and above) that are interested in BN or Opposition parties:

  • 54% are male and 46% are female
  • 3 million are interested in Opposition parties
  • 8 million are interested in BN parties
  • 76% (400 thousand) are exclusively interested in Opposition parties
  • 52% (2.9 million) are interested in a mix of Opposition and BN parties
  • 71% (5.1 million) are exclusively interested in BN parties

As of August 2016 the level of exclusive interest in 60.71% for BN and 4.76% for the Opposition. This is a record high for BN and a record low for the Opposition since we began tracking these statistics in December 2012.

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Written by politweet

September 1, 2016 at 9:44 am

Evaluating Voter Support in Kuala Kangsar and Sungai Besar Using General Election Results and Twitter

1. Background

Prior to the 13th General Election (GE13) we came up with a methodology of predicting election results based on voting patterns in previous elections.

Our method relied on mapping polling lane results to individual voters. This process assigned probability values (chance of turnout; chance of voting for each coalition) to the voter that was not affected if they migrated to another constituency. This is important because between GE12 and GE13 527,849 voters migrated to different constituencies.

The impact of voter migration cannot be measured for a single seat by comparing the results of GE12 and GE13 for that seat. An analysis of the whole country needs to be performed. New voter registrations, voters passing away and voters no longer eligible to vote are other factors that require deep analysis.

After GE13 we were able to apply the same estimation method to voters based on GE13 results. By comparing the shift in probabilities we are able to calculate the swing in support for each coalition. Because we base our calculations on individual voters, we are able to calculate shifts in support based on combinations of the following dimensions:

  • By Age
  • By Race
  • By Gender
  • By Urban Development Category (rural / semi-urban / urban)
  • By Parliament/State Assembly Seat
  • By Polling District
  • By Locality
  • By Seats Won by Specific Parties

Any voter whose level of support cannot be determined is assigned a probability of 50% and categorised as a fence-sitter. The most reliable metric is age because voters are separated into polling lanes based on age. Additionally we have also categorised the 222 Parliament constituencies as rural, semi-urban or urban based on satellite imagery. The descriptions of each category are:

Rural = villages (kampungs) / small towns / farmland distributed within the seat. Rural seats tend to be physically large with a low population.

Semi-urban = larger towns and/or numerous small towns, may include villages as well

Urban = cities where a majority of the seat is covered by some form of urban development

For this report we will focus on how Pakatan Rakyat (PR) and Barisan Nasional (BN) performed with regular voters (pengundi biasa) in P67.Kuala Kangsar and P93.Sungai Besar. This will give a sense of what to expect during the by-elections to be held on June 18th 2016.

In addition to this we will also briefly examine political interest from Twitter users based in these constituencies. This may identify patterns that can be linked to urban youth in these areas.

Postal and early voters are not part of this analysis. They need to be analysed separately due to their different voting process and difficulties in campaigning to both groups.

Please remember that unless otherwise stated, all statistics in this analysis refer to regular voters only. We do not have access to the electoral roll being used for these by-elections and will be relying on estimated figures from the electoral roll for 2015 Q4 (4th quarter).

2. Seat Demographics

Demographics for Sungai Besar and Kuala Kangsar are listed below.

Detail / Seat P93. Sungai Besar P67. Kuala Kangsar
State

 

Selangor

 

Perak

 

Voters (GE13)

 

 

42,923

(2.09% of Selangor voters)

 

 

33,607

(2.38% of Perak voters)

 

Urban Development Category

 

Rural

 

Semi-urban

 

Majority Race

 

Malay

 

Malay

 

Contesting Parties (GE13)

 

UMNO, PAS

 

UMNO, PAS, Independent

 

Winner (GE13)

 

UMNO

 

UMNO

 

 

Twitter Users

 

 

 

1,049

(0.66% of Selangor users)

89% primarily use Bahasa Malaysia

 

660

(2.39% of Perak users)

81% primarily use Bahasa Malaysia

 

The following charts show the estimated ethnic divide among voters in both seats based on our estimated electoral roll for 2015 Q4. This covers all voters (postal, early and regular).

sgbesar_ethnicpie

kkangsar_ethnicpie

Changes in Sungai Besar since GE13 (up to 2015 Q4):

  • Malay voters increased by 0.33 percentage points
  • Chinese voters decreased by 0.4 percentage points
  • Indian voters increased by 0.06 percentage points
  • 1,260 voters removed
  • 1,394 new voters
  • 171 voters migrated in from other constituencies

 

Changes in Kuala Kangsar since GE13 (up to 2015 Q4):

  • Malay voters increased by 0.46 percentage points
  • Chinese voters decreased by 0.45 percentage points
  • Indian voters decreased by 0.015 percentage points
  • 1,153 voters removed
  • 1,079 new voters
  • 185 voters migrated in from other constituencies

Both seats have had an increase in the percentage of Malay voters, and a decrease in the percentage of Chinese voters.

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Written by politweet

June 11, 2016 at 9:25 am

Popular Chinese New Year Content Shared By Users in Malaysia (2016)

cny_top_media_2016

The first day of Chinese New Year was celebrated worldwide on February 8th 2016. We collected tweets mentioning Chinese New Year in English and Bahasa Malaysia from February 7th – February 12th 2016. This was limited to the most common phrases – ‘Tahun Baru Cina’, ‘Chinese New Year’, ‘Gong Xi Fa Cai’, ‘CNY’ and similar spellings. Our focus was on identifying the content that was widely shared and the demographics of users tweeting Chinese New Year greetings.

From the tweets collected we prepared listing of popular tweets and media shared by users in Malaysia, limited by the language used. More details on the methodology used are at the end of this post.

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Written by politweet

February 25, 2016 at 12:36 pm

Key Topics, Influencers, Misinformation and How They Changed Perception towards 1MDB and Najib

1. Introduction

On August 3rd 2015 the Malaysian Anti-Corruption Commission (MACC) released a statement saying that the alleged transfers of RM2.6 billion into Prime Minister Najib Razak’s personal account came from donors and not 1MDB (link1 , link2).

This prompted a strong response on Twitter as users started to tweet about Najib and derma (donations).

We collected 193,893 tweets from 79,949 users about donations (derma) and Najib from August 3rd – August 6th. To get a sense of the scale, here is the graph for mentions of Najib and donations per day within the past 30 days.

topsy_derma_vs_najib_Aug8
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Written by politweet

August 18, 2015 at 3:29 pm

Opinion Analysis of Twitter Users in Malaysia on Muhyiddin Yassin’s Dismissal

1. Background

On July 28th 2015 a new Cabinet line-up was announced by Malaysia’s Prime Minister Najib Razak.

Former Deputy Prime Minister Tan Sri Muhyiddin Yassin was absent from the new Cabinet. Zaid Hamidi replaced him for the post of Deputy Prime Minister and Mahdzir Khalid replaced him for the post of Minister of Education.

Muhyiddin being dropped from the Cabinet followed speculation that this would happen due to his remarks about 1MDB on Sunday, July 26th. He was reported to have asked for PM Najib to personally explain the 1MDB issue and answer the allegations by the Wall Street Journal (WSJ) [1].

2. Our Analysis

We performed opinion-based analysis on 502 users based in Malaysia who tweeted about Muhyiddin and related terms on July 28th 2015. The margin of error is +/- 4.37%.

Users were selected based on their tweet content and activity during this period. Sampling was done per-state based on the current estimated user population.

Spammers, news agencies and accounts with automated tweets were not included in the sample.

From this dataset we analysed the individual Twitter user timelines to determine their opinion. This took their tweets, retweets and conversations into account.

Our goal was to gauge public reaction by Twitter users in Malaysia to the removal of Tan Sri Muhyiddin Yassin to see if they supported the decision.

Users who were tweeting about related topics (PM Najib; the Cabinet line-up) but not in response to Muhyiddin Yassin’s dismissal were not included in the sample. However we took note of what these users were saying for section 4 (Additional Opinions).

Based on this analysis we categorised users as belonging to one of the following categories:

  1. Positive
  2. Neutral
  3. Negative (support Muhyiddin)

These were further divided into the following categories based on which party they chose to support/criticise:

  1. Positive
  2. Neutral
  3. Negative (support Muhyiddin)
  4. Negative (support Muhyiddin, critical of Najib)
  5. Negative (support Muhyiddin, he must oppose Najib)

The results are shown in the following charts.
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Written by politweet

August 3, 2015 at 10:58 am