Posts Tagged ‘General Election’
Election Forecast for Pakatan Harapan in Peninsular Malaysia (GE14)
1. Introduction
On January 10th 2018 a detailed listing of the 165 seats being contested by Pakatan Harapan (PH) component parties in Peninsular Malaysia was published in The Star and Malaysiakini (https://www.malaysiakini.com/news/408114). Based on this listing, the seat division is as follows:
- 52 seats contested by PPBM
- 51 seats contested by PKR
- 35 seats contested by DAP
- 27 seats contested by AMANAH
This report provides an overview of the seats contested by component parties in PH, the winning odds and forecasted results for each seat under a number of different scenarios. Analysis was performed based on the 2017 1st Quarter (Q1) electoral roll, 2016 3rd Quarter (Q3) electoral roll, State and Federal seat results from the 13th General Election (GE13) and individual historical voting patterns from GE12 (2008) and GE13 (2013).
A copy of this report in PDF format can be downloaded at https://drive.google.com/file/d/16glv67fwRybQxdKS7X1fFC9R1SN0TSGy/view?usp=sharing
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:
- Total population refers to Facebook users aged 13 years and above.
- Potential voters refer to Facebook users aged 21 years and above.
- Youth refers to Facebook users aged 13 – 20 years.
- 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.
- Detailed statistics on Putrajaya are not available due to the small number of users in the territory.
- 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.
- Facebook users residing in Malaysia are not necessarily Malaysian citizens.
- Interest in a topic is equal to the number of users expressing interest in a topic.
- 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).
- 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
- Interest in a political party does not indicate support for the party, only awareness
- It is currently assumed that interest in PAS includes some interest in AMANAH as PAS leaders and members migrated to AMANAH
- 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).
- 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.
- Statistics on the Opposition primarily refer to component parties of the former Pakatan Rakyat – PKR, PAS and DAP. This includes the ‘Pakatan’ brand name.
- 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.
- 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.
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:
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.
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
How Barisan Nasional and Pakatan Rakyat Performed With Voters in Sarawak (GE13)
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 just by comparing 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 Sarawak. 31 of the total 222 Parliament seats are in Sarawak.
Our analysis will focus on Malay, Chinese and Bumiputera Sarawak voters. Other ethnic groups such as Indians, Orang Asli and Bumiputera Sabah voters will be counted under the ‘Others’ category unless otherwise specified. This is due to their low numbers within the electorate and the lack of detail within the National Census data.
Postal and early voters are not part of this analysis, other than the section on polling lanes. Postal voters need to be analysed separately due to their different voting process and difficulties in campaigning to both groups.
The predicted support for PR based on GE12 was estimated to be low. This is because in GE12 the PR component parties did not contest all seats. SNAP and Independents contested BN in some seats with no PR candidates. There were also seats that were won by BN uncontested. PR was effectively untested in Sarawak.
We tested analysis using SNAP and Independent results as ‘pro-Opposition’ in place of PR. However this approach made little impact on the analysis. A vote for SNAP or Independents also cannot be assumed as a vote for PR. To keep analysis consistent with a ‘BN versus PR’ perspective we did not treat SNAP and Independent candidate results as PR results.
We will also present analysis of seats at the state (DUN) level based on individual voting at the Parliament level. It is not as accurate as performing analysis based on state-level results but it should be applicable for constituencies where voters voted for the same coalition (BN / PR) for both state and Parliament.
Please remember that unless otherwise stated, all statistics in this analysis refer to regular voters in Sarawak only.
Analysing Pakatan Rakyat’s Performance with Malay Voters in Peninsular Malaysia (GE13)
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 (reference).
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 just by comparing 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 (reference). 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 Malay voters (pengundi biasa) in Peninsular Malaysia. 184 of the total 222 Parliament seats are in Peninsular Malaysia, where most of the Malay electorate is concentrated.
Elections are won based on the number of seats. However our analysis will mainly be on the Malay electorate treated as a set of voters ignoring constituency boundaries. We will examine this at the state-level and for Peninsular Malaysia. This will allow us to see patterns that are not obvious at the seat-level.
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 in Peninsular Malaysia only.
Urban Development Categorisation of Parliament Seats in Malaysia
The list of Parliament seats in Malaysia by urban development category can be found here.
For further reading on the methodology please read this blog post.
The categories 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
This classification was done by us based on Google Maps satellite imagery and SPR maps. This is not the same as SPR’s own internal classification of seats. You can find our reference maps here (link).
We have obtained a gridded map of Malaysia that defines persons per square kilometre, based on a grid cell size of 19 square kilometres. This population estimate is based on an extrapolation of the 2000 National Census. This map will enable us to :
- Estimate the size of the constituency
- Estimate the min, max and average population density
- Define a range of rural, semi-urban and urban categories, instead of just three
- Perform seat classification at the State Assembly Seat (DUN) level
However this is time consuming and will likely take months to prepare. A sample of the Peninsular Malaysia map is shown below, rendered as a heatmap ranging from Green (low population density) -> Yellow -> Orange -> Red -> Purple (highest population density). The SPR maps are overlaid over it, and by toggling visibility we can identify which cells belong to which constituency. For area calculation we can subdivide the 19 square kilometre grid into 4.75 or 1.1875 square kilometre grids, depending on how small the related constituency is.
Coverage: (Yahoo News) How is social media affecting the 13th Malaysian general elections?
Published on May 1st, 2013 (link to original story) (Indonesian version)
by Susan Tam
Malaysia’s political landscape is facing one of the most fiercely contested elections in history, and you’re able to witness this through the displays of hundreds of flags, buntings and banners as well as from the noisy ceramahs of the various parties vying for your vote. Well, the ‘noise’ levels on cyberspace similarly, has gone up with the rise of social media usage. We decided to find out what it all means by having a chat with social media research firm, Politweet.
From 2,400 people tweeting about politicians in 2010 to 450,000 users chatting about politics on Twitter, three years later.
That is how much Malaysia’s Twitter base has grown, reflecting how much the interest in politics has grown too.
We spoke to Politweet’s Ahmed Kamal to find out if the noise being made on social media should matter in the upcoming elections. Politweet is a non-partisan research company that specialises in the analysis of interactions of Malaysians using social media. One of the goals of the firm is to develop a set of tools for other researchers and journalists to analyse social media.
“The social media usage has definitely increased. We have one to two million Twitter users in Malaysia and over 13 million Facebook users, with over nine million of those above 21 years old.
“In 2008, people mainly turned to blogs for political information. Today the conversation seems to have moved to Facebook and Twitter.”
Seeing this is a technological age, and that more people are getting connected, it’s a no-brainer that more Malaysians have created Twitter or Facebook accounts so they could keep up with the trends. But what has that done for political parties, particularly during hot election campaign periods?
Ahmed, in his research, finds that like its political rival, BN is making use of Facebook and Twitter in its political campaigns. The ‘problem’ though, lies in how they are using it.
“You don’t have many supporters that are having conversations with users and trying to build up that rapport (with users) to convince them to switch over to your side. Instead they are trying to employ methods, like flooding the channels with links to articles or blogs or news about what BN politicians are doing.”
That is a different style to how PR is using these tools to connect to their constituents.
“Maybe they don’t have as many paid people doing it, but whatever (PR) supporters they do have, they are usually strong and they will invest time in conversations that help to change people’s minds.”
He is certain of the fact that BN and PR use this new medium differently in their efforts to spread their political messages, which affects the results of the research on social media.
But who is winning this battle?
Ahmed does not even see an even playing field to begin with, for these two alliances.
“It is very hard to measure the competition between PR and BN. Based on the tweet levels (of both coalitions), BN does not seem to be trying very hard.”
Normally, in political campaigns, BN would lead PR by big margins in terms of mentions. But the results this time indicate both alliances are close to each other.
“It does give me the impression that they (BN) have shifted their attention towards traditional media instead of social media.”
There is a sense that BN could be falling back on the ‘old’ media or traditional media to push their messages, which is something that Ahmed did expect.
With close to 10 million Facebook users possibly becoming first time voters on May 5, we wanted to find out if this was a wise strategy.
Ahmed feels BN isn’t pushing social media as ‘hard as they could’, and people can see their messages through the many billboards, buntings and newspaper advertisements displayed across traditional channels. PR does not have the resources to do the same.
“It does seem like a battle between old media and new media, but we won’t know how effective this is until after the elections.”
He was quick to point out as well that being very popular on cyberspace does not necessarily translate into votes for politicians or parties.
Prime Minister Najib Razak trumps Opposition Leader Anwar Ibrahim in the number of Twitter followers, despite having a larger number of inactive followers or ‘manufactured accounts’.
Based on Politweet’s census done in February, Najib’s has 1.2 million followers, with only 48% being active accounts.
Anwar has 236,000, with a higher percentage of active accounts of 61%.
But if you break down the numbers, Anwar’s genuine 144,000 followers are tiny compared to Najib’s 582,000.
Ahmed Kamal finds that Najib’s popularity is also the driving force behind BN’s overall popularity. PR on the whole has 59% active followers, compared to BN’s 43%. But, if you take Najib out of the equation, it could mean a different scenario.
According to reports, Ahmed Kamal joined PKR in 1999 but was inactive after that year’s elections. He worked for Anwar in 2007 and did some freelance work for PR after March 2008.
He now focuses on remaining non-partisan and began monitoring social media trends in 2009. Politweet’s detailed research can be found here (http://www.politweet.org/site/main.php).
