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).
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 .
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.” 
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:
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.
We then performed opinion-based analysis on 520 users based in Kuala Lumpur, Selangor and Penang. The margin of error is +/- 4.3%.
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.
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 . TPPA negotiations began in March 2010 with Malaysia becoming the 9th member in October 2010. The countries involved in the agreement are:
- United States
- New Zealand
In Malaysia 2 anti-TPPA protest rallies (called #BantahTPPA) were held on January 23rd 2016 :
- 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 .
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:
- 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:
- Fear of Colonisation & Loss of Sovereign Rights
- Exaggerated Fears / Propaganda
- Competition & Foreign Labour
- Distrust of Government / BN
- Increases in Price of Medicine
- Economic Burden Similar to GST
- Islamic Reasons
The results are shown in the following charts.