The Impact of Redelineation On The Selangor State Elections

1. Introduction

On September 15^th 2016 the Election Commission of Malaysia (Suruhanjaya Pilihanraya Malaysia) published the proposed redelineation of electoral boundaries for State and Federal constituencies. Under this proposal:

• No new Federal constituencies would be created
• 13 new State constituencies would be created in Sabah
• No new State constituencies would be created in states other than Sabah
• 12 Federal constituencies would be renamed
• 36 State constituencies would be renamed

This report provides an overview of the impact of state constituency redelineation on the Selangor State elections. Analysis was performed based on the 2016 1^st Quarter (Q1) electoral roll (before and after redelineation), State and Federal seat results from the 13^th General Election (GE13) and individual historical voting patterns from GE12 (2008) and GE13 (2013).

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.

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.

Analysis of Opinions on Lim Guan Eng’s Corruption Charges by Twitter Users in Malaysia

1. Background

On March 17^th 2016, Shabudin Yahaya (BN MP for Tasek Gelugor) alleged that the Chief Minister of Penang, Lim Guan Eng was involved in the sale of two plots of land in Taman Manggis, Penang to a company whose owner was connected to the owner of a bungalow purchased by Lim Guan Eng. The purchase of the bungalow was alleged to have been below the market-price [1].

Comparisons were made between these allegations and former Menteri Besar of Selangor, Khir Toyo’s case where he was convicted for abusing his power to acquire land and property below the market-price.

Following these allegations, a series of exposes and more allegations surfaced online. MACC conducted investigations and on June 29^th Lim Guan Eng was arrested and held overnight.  On June 30^th he was charged with corruption in the Penang High Court. His former land-lady, Phang Li Koon was also charged for her involvement.

The charges faced by Lim Guan Eng are described below:

“Lim is facing two charges for corruption – one under Section 165 of the Penal Code and another Section 23 of the Malaysian Anti-Corruption Act (MACC) 2009 — over his approval of an application from Magnificent Emblem to convert a piece of land from agricultural to residential use, as well as over his purchase of a house from the firm’s director, Phang, for RM2.8 million, which was below the property’s market value of RM4.27 million.” [2]

Since the story broke in March we tracked mentions of Lim Guan Eng, Taman Manggis, Khir Toyo and other related terms to gauge the response to the initial story and on-going exposes.

2. Initial Analysis (March 17^th – April 30^th)

We initially examined tweets by 10,627 users from March 17^th – April 30^th 2016 mentioning the keywords related to allegations against Lim Guan Eng.

What we found was the topic was popular mainly with users with a strong partisan interest in Malaysian politics. This issue did not draw enough interest from the general public – it was not worth talking about, and those who did tended to express disinterest or only retweet news articles.

The topic also drew more interest from users based in Kuala Lumpur, Selangor and Penang. 59% of users tweeting about the topic (not including retweets) were based in these 3 states. The highest drop in interest was from users in Johor, which made up 8.63% of the local population (1.96 points lower than the proportional average).

There was also a high degree of spammed tweets, with spammed tweets outnumbering non-spammed tweets on some days. This can be seen in the chart below:

1,358 users spammed 49,223 tweets. In other words, 12.8% of the users spammed 41.8% of the total tweets.

From a manual reading of non-spammed tweets during this period, we found that tweeted opinions about the scandal fell mainly into the following categories:

• Users not interested in Lim Guan Eng’s scandal
• Users complaining about excessive media coverage. Most complaints implied users were bored or not interested in listening to the repeated allegations.
• Users wanting Lim Guan Eng to be investigated
• Users comparing Lim Guan Eng’s case with Khir Toyo’s case
• Users criticising Lim Guan Eng’s responses to the allegations
• Users critical of BN and DAP, equating both to be corrupt
• Users defending Lim Guan Eng. Among the more popular reasons were:
  • BN / UMNO / PM Najib are considered to be worse
  • The discount isn’t that big / there is nothing wrong with a good deal
  • The 1MDB scandal is much bigger and more important than Lim Guan Eng’s scandal
  • Khir Toyo’s house is bigger

Users defending Lim Guan Eng were a small minority. There was little evidence of pro-DAP or pro-Opposition users being mobilised to defend Lim Guan Eng.

Out of 44 DAP politicians actively tweeting in this period, only 27 politicians tweeted/retweeted tweets mentioning Lim Guan Eng or keywords related to the allegations. This does not include images or tweets not mentioning related keywords. By not talking about the allegations the 17 politicians missed an opportunity to contribute to Lim Guan Eng’s defence on Twitter.

Because of the low level of interest from the general public and the high degree of spam, we could not do a detailed opinion analysis at the time.

3. Analysis of Opinions on Lim Guan Eng’s Corruption Charges (June 29^th – July 6^th)

We examined tweets by 8,365 users from June 29^th – July 6^th 2016 mentioning keywords related to allegations against Lim Guan Eng. The daily interest is shown in the graph below.

We then performed opinion-based analysis on 520 users based in Kuala Lumpur, Selangor and Penang. The margin of error is +/- 4.3%.

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 18^th 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 (4^th 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).

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.

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.

Analysis of Support for the TPPA by Twitter Users in Malaysia

1. Background

The Trans-Pacific Partnership Agreement (TPPA/TPP) is a free trade agreement between 12 countries with a combined market of 800 million people and combined GDP of USD 27.5 trillion [1]. TPPA negotiations began in March 2010 with Malaysia becoming the 9^th member in October 2010. The countries involved in the agreement are:

• Australia
• Brunei
• Canada
• Chile
• Malaysia
• Japan
• Peru
• United States
• Mexico
• New Zealand
• Singapore
• Vietnam

In Malaysia 2 anti-TPPA protest rallies (called #BantahTPPA) were held on January 23^rd 2016 [2]:

• A PAS-led protest held at Padang Merbok (KL) and estimated to have 4,000 protesters
• A protest composed of student activists, civil society leaders, Opposition party leaders and supporters along Jalan Parlimen near Dataran Merdeka. The crowd was estimated to contain 500 protesters.

After a 2-day debate in Parliament the TPPA was approved on January 27^th. The TPPA was signed in Auckland, New Zealand on February 4^th [3].

 

2. Our Analysis

We performed opinion-based analysis on 600 users based in Malaysia who tweeted about the TPPA and related terms from January 18^th – February 8^th 2016. The margin of error is +/- 4%.

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. Only users who had an opinion about the TPPA were used in the sample.

Our goal was to gauge public support by Twitter users in Malaysia for the TPPA. As this was a complex trade agreement our expectation was that the result would be heavily weighted towards not supporting the TPPA.

This is because the average person would find the document difficult to comprehend and relate to their own interests. It would be easier to dismiss it and not comment on it. Conversations about the TPPA would therefore likely be driven by politically partisan people and users looking for simple answers. Given the level of distrust of government sources of information, it is possible for such users to be manipulated.

Therefore the percentage of users opposing the TPPA has less value than the details. Identifying the most popular reasons for opposing the TPPA would prove insightful.

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

1. Support
2. Neutral
3. Don’t Support

Users who did not support the TPPA expressed a variety of reasons. Based on samples of the data we determined the most frequently mentioned reasons. The popular reasons for opposing the TPPA were then grouped into the following categories:

1. Fear of Colonisation & Loss of Sovereign Rights
2. Exaggerated Fears / Propaganda
3. Competition & Foreign Labour
4. Distrust of Government / BN
5. Increases in Price of Medicine
6. Economic Burden Similar to GST
7. Islamic Reasons

The results are shown in the following charts.

Popular Chinese New Year Content Shared By Users in Malaysia (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.