Politweet.Org

Observing Malaysian Social Media

Archive for the ‘Analyses’ Category

The Impact of Redelineation On The Selangor State Elections

1. Introduction

On September 15th 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 1st Quarter (Q1) electoral roll (before and after redelineation), State and Federal seat results from the 13th General Election (GE13) and individual historical voting patterns from GE12 (2008) and GE13 (2013).

Read the rest of this entry »

Advertisements

Written by politweet

November 9, 2016 at 2:58 am

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

1. Background

On March 17th 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 29th Lim Guan Eng was arrested and held overnight.  On June 30th 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 17th – April 30th)

We initially examined tweets by 10,627 users from March 17th – April 30th 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:

lgearrest_mac_interest

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 29th – July 6th)

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

lgearrest_jun_interest

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

Read the rest of this entry »

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.

Read the rest of this entry »

Written by politweet

June 11, 2016 at 9:25 am

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.

Read the rest of this entry »

Written by politweet

April 16, 2016 at 9:12 pm

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 9th 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 23rd 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 27th. The TPPA was signed in Auckland, New Zealand on February 4th [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 18th – February 8th 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.

Read the rest of this entry »

Written by politweet

April 10, 2016 at 12:17 pm

Evaluating the Response to the National Security Council Bill by Twitter Users in KL and Selangor

1. Background

The National Security Council (NSC) 2015 Bill was tabled in the Dewan Rakyat on December 1st [1]. The purpose of the bill is, “to establish the National Security Council with powers, among others, to control and coordinate, and to issue directives to, the Government Entities on matters concerning national security. The proposed Act also empowers the Prime Minister, upon advice by the Council, to declare certain area in Malaysia as a security area. Special powers are given to the Security Forces in the security area.” [2]

Within a security area, security forces may:

  • evacuate persons from the area
  • enforce a curfew on all persons within the area
  • control the movement/entry/exit of persons and vehicles
  • arrest any person suspected of committing an offence (without a warrant)
  • stop and search any person, vehicle or premises (without a warrant)
  • seize any vehicle if it is suspected to have been used in the commission of an offense
  • take temporary possession of land, building or movable property in the interest of national security or as accommodation for security forces; with conditional compensation to aggrieved persons
  • demolish unoccupied buildings that may be used by persons who are a threat to national security; with conditional compensation to aggrieved persons

Additionally the Council, its members, the security forces or personnel of Government entities are protected from legal action.

The combination of a lack of accountability and enforcement powers given to the PM were highlighted by the Opposition and civil society members.

Following the passing of the bill on December 4th [3], the #TakNakDiktator campaign on December 8th [4]. The goal of the campaign is to spread awareness of the issue and stop the bill from being made into law.

As of December 20th, 22,552 supporters have signed the online petition and 6,481 users have tweeted the #TakNakDiktator hashtag.

Read the rest of this entry »

Written by politweet

December 24, 2015 at 3:46 pm

Response to The Race of Bersih 4 Protesters by Twitter Users in Peninsular Malaysia

1. Background

From August 29th – August 30th a rally entitled ‘Bersih 4’ was held on the streets of Kuala Lumpur, Kuching, Kota Kinabalu and other locations globally.

The demands of the rally were for Prime Minister Najib Razak to step down and a transitional government to be formed. This government would need to implement 10 institutional reforms within the next 18 months to ensure the next General Election would be conducted in a clean, free and fair manner:

  1. Reform of electoral system and process
  2. Reform of the Election Commission (EC)
  3. Separation of Prime Minister and Finance Minister
  4. Parliamentary Reform
  5. Separation of the functions of Attorney General and Director of Public Prosecution
  6. Reform of the MACC
  7. Freedom of Information laws
  8. Asset declaration by Ministers and senior state officials
  9. Abolishment of/Amendment to draconian laws
  10. Establishment of the Independent Police Complaints and Misconduct Commission (IPCMC)

During the first day of the rally the race of the participants in Kuala Lumpur was raised as an issue by the media and by social media users. It was clear that the majority of the protesters were ethnically Chinese. The ethnic majority was also reported by Malaysiakini [1], Utusan Malaysia [2] and Berita Harian [3].

By our own estimates, 79,919 – 108,125 people attended the Kuala Lumpur rally over the 2-day period. Based on photographs seen during our crowd estimation, we would roughly estimate that 60% – 80% of the protesters were ethnically Chinese.

The race of the protesters became an issue due to media reports and Bersih 4 supporters and detractors highlighting the race of the protesters. This provoked a response by users on Twitter as they tweeted their own opinions on the rally.

2. Our Analysis

We performed opinion-based analysis on 500 users based in Peninsular Malaysia who tweeted about Bersih 4 (and related terms), race (e.g. ‘Melayu’, ‘Cina’, ‘Malay’, ‘Chinese’), racism and related terms from August 29th – September 2nd 2015. The margin of error is +/- 4.38%.

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.

Users who were only observing the number of Chinese present were not included in the sample. This was because we wanted to gauge their opinion on the Chinese majority and whether it was an issue to them.

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

One issue we encountered was a lack of users in East Malaysia tweeting about Bersih 4 and racial terms. Both sets of data were too limited to consider using for analysis. For this analysis we only focused on users in Peninsular Malaysia.

Our goal was to gauge the response by Twitter users in Peninsular Malaysia to the race of protesters at the Bersih 4 rally in Kuala Lumpur. Was the race of protesters really an issue, and if so, why?
Read the rest of this entry »

Written by politweet

October 8, 2015 at 10:47 am

Posted in Analyses

Tagged with , , , , , , , ,