This record contains the underlying research data for the publication "Extended Comprehensive Study of Association Measures for Fault Localization" and the full-text is available from: https://ink.library.smu.edu.sg/sis_research/1818Spectrum-based fault localization is a promising approach to automatically locate root causes of failures quickly. Two well-known spectrum-based fault localization techniques, Tarantula and Ochiai, measure how likely a program element is a root cause of failures based on profiles of correct and failed program executions. These techniques are conceptually similar to association measures that have been proposed in statistics, data mining, and have been utilized to quantify the relationship strength between two variables of interest (e.g., the use of a medicine and the cure rate of a disease). In this paper, we view fault localization as a measurement of the relationship strength between the execution of program elements and program failures. We investigate the effectiveness of 40 association measures from the literature on locating bugs. Our empirical evaluations involve single-bug and multiple-bug programs. We find there is no best single measure for all cases. Klosgen and Ochiai outperform other measures for localizing single-bug programs. Although localizing multiple-bug programs, Added Value could localize the bugs with on average smallest percentage of inspected code, whereas a number of other measures have similar performance. The accuracies of the measures in localizing multi-bug programs are lower than single-bug programs, which provokes future research.

These files are used to replicate all analyses in Media in a Time of Crisis: Newspaper Coverage of Covid-19 in East Asia, available at https://ink.library.smu.edu.sg/soss_research/3348/.

This dataset comprises a set of Twitter accounts in Singapore that are used for social bot profiling research conducted by the Living Analytics Research Centre (LARC) at Singapore Management University (SMU). Here a bot is defined as a Twitter account that generates contents and/or interacts with other users automatically (at least according to human judgment). In this research, Twitter bots have been categorized into three major types:

Broadcast bot. This bot aims at disseminating information to general audience by providing, e.g., benign links to news, blogs or sites. Such bot is often managed by an organization or a group of people (e.g., bloggers). Consumption bot. The main purpose of this bot is to aggregate contents from various sources and/or provide update services (e.g., horoscope reading, weather update) for personal consumption or use. Spam bot. This type of bots posts malicious contents (e.g., to trick people by hijacking certain account or redirecting them to malicious sites), or promotes harmless but invalid/irrelevant contents aggressively.

This categorization is general enough to cater for new, emerging types of bot (e.g., chatbots can be viewed as a special type of broadcast bots). The dataset was collected from 1 January to 30 April 2014 via the Twitter REST and streaming APIs. Starting from popular seed users (i.e., users having many followers), their follow, retweet, and user mention links were crawled. The data collection proceeds by adding those followers/followees, retweet sources, and mentioned users who state Singapore in their profile location. Using this procedure, a total of 159,724 accounts have been collected. To identify bots, the first step is to check active accounts who tweeted at least 15 times within the month of April 2014. These accounts were then manually checked and labelled, of which 589 bots were found. As many more human users are expected in the Twitter population, the remaining accounts were randomly sampled and manually checked. With this, 1,024 human accounts were identified. In total, this results in 1,613 labelled accounts. Related Publication: R. J. Oentaryo, A. Murdopo, P. K. Prasetyo, and E.-P. Lim. (2016). On profiling bots in social media. Proceedings of the International Conference on Social Informatics (SocInfo’16), 92-109. Bellevue, WA. https://doi.org/10.1007/978-3-319-47880-7_6

This record contains the underlying research data for the publication "Understanding the effects of taxi ride-sharing: A case study of Singapore" and the full-text is available from: https://ink.library.smu.edu.sg/sis_research/3968This paper studies the effects of ride-sharing among those calling on taxis in Singapore for similar origin and destination pairs at nearly the same time of day. It proposes a simple yet practical framework for taxi ride-sharing and scheduling, to reduce waiting times and travel times during peak demand periods. The solution method helps taxi users save money while helping taxi drivers serve multiple requests per day, thus increasing their earnings. A comprehensive simulation study is conducted, based on real taxi booking data for the city of Singapore, to evaluate the effect of various factors of the ride-sharing practice, e.g., waiting time, extra travel time, and taxi fare reduction. The results demonstrate that ride-sharing could serve 20%–25% more taxi booking requests and reduce traveler waiting time during peak hours. It also indicates that there is a reduction in travel distance of approximately 2–3 km per taxi trip on average, which is approximately 20%–30% of the average ride distance.

This dataset comprises a set of information cascades generated by Singapore Twitter users. Here a cascade is defined as a set of tweets about the same topic. This dataset was collected via the Twitter REST and streaming APIs in the following way. Starting from popular seed users (i.e., users having many followers), we crawled their follow, retweet, and user mention links. We then added those followers/followees, retweet sources, and mentioned users who state Singapore in their profile location. With this, we have a total of 184,794 Twitter user accounts. Then tweets are crawled from these users from 1 April to 31 August 2012. In all, we got 32,479,134 tweets. To identify cascades, we extracted all the URL links and hashtags from the above tweets. And these URL links and hashtags are considered as the identities of cascades. In other words, all the tweets which contain the same URL link (or the same hashtag) represent a cascade. Mathematically, a cascade is represented as a set of user-timestamp pairs. Figure 1 provides an example, i.e. cascade C = {< u1, t1 >, < u2, t2 >, < u1, t3 >, < u3, t4 >, < u4, t5 >}. For evaluation, the dataset was split into two parts: four months data for training and the last one month data for testing. Table 1summarizes the basic (count) statistics of the dataset. Each line in each file represents a cascade. The first term in each line is a hashtag or URL, the second term is a list of user-timestamp pairs. Due to privacy concerns, all user identities are anonymized.

This is the replication data for the paper "The Intergenerational Mortality Tradeoff of COVID-19 Lockdown Policies" by Lin Ma, Gil Shapira, Damien de Walque, Quy-Toan Do, Jed Friedman, and Andrei Levchenko.

For steps to replicate the results, please refer to the readme file included alongside the data files.

This record contains the replication data for "Geography, Trade, and Internal Migration in China" published in Journal of Urban Economics, Volume 115 in Jan 2020. codes_data_publish.zip contains the Full Package for replication and Maps and map_publish_jue_2019.zip contains the Maps and Transportation Matrix only.

This record contains the underlying research data for the publication "The Valuation of User-Generated Content: A Structural, Stylistic and Semantic Analysis of Online Reviews" and the full-text is available from: https://ink.library.smu.edu.sg/etd_coll/78The ability and ease for users to create and publish content has provided vast amount of online product reviews. However, the amount of data is overwhelmingly large and unstructured, making information difficult to quantify. This creates challenge in understanding how online reviews affect consumers’ purchase decisions. In my dissertation, I explore the structural, stylistic and semantic content of online reviews. Firstly, I present a measurement that quantifies sentiments with respect to a multi-point scale and conduct a systematic study on the impact of online reviews on product sales. Using the sentiment metrics generated, I estimate the weight that customers place on each segment of the review and examine how these segments affect the sales for a given product. The results empirically verified that sentiments influence sales, of which ratings alone do not capture. Secondly, I propose a method to detect online review manipulation using writing style analysis and assess how consumers respond to such manipulation. Finally, I find that societal norms have influence on posting behavior and significant differences do exist across cultures. Users should therefore exercise care in interpreting the information from online reviews. This dissertation advances our understanding on the consumer decision making process and shed insight on the relevance of online review ratings and sentiments over a sequential decision making process. Having tapped into the abundant supply of online review data, the results in this work are based on large-scale datasets which extend beyond the scale of traditional word-of-mouth research.

This collection contains 69 audio records of interviews with social workers, government officials, and academia from China, Hong Kong, and Singapore conducted between 2009 to 2012. The interviewees have different designations and are from different social service organizations, government departments, and universities/colleges. The themes of the interviews cover the status quo of social workers, how social services organizations operate, their relationships with the government, and their main functions. The audio interviews are predominantly in Mandarin and Cantonese, with a few in English. The size of the audio collection is 2.33GB. For access to the audio interviews please contact Professor Chung Wai Keung for permission.

This e-companion contains four sets of supporting materials for the main paper. EC.1 provides algorithmic treatments to handle key market implementation issues. EC.2 examines effects of active market intermediation on market performance and the dealer’s wealth under the controlled market experiment. EC.3 studies market liquidity and heterogeneous market participation in a randomized market environment. EC.4 includes proofs of Lemmas and Corollaries.

Dataset for investigating Singapore residents' attitudes towards organisations’ use of artificial intelligence and data mining.

This record contains the underlying data/supplementary materials/appendix for the publication "Socially responsible firms" published in Journal of Financial Economics in 2016.

This record contains the underlying research data for the publication "Acknowledging individual responsibility while emphasizing social determinants in narratives to promote obesity-reducing public policy: A randomized experiment" and the full-text is available from: https://ink.library.smu.edu.sg/lkcsb_research/4848This study tests whether policy narratives designed to increase support for obesity-reducing public policies should explicitly acknowledge individual responsibility while emphasizing social, physical, and economic (social) determinants of obesity. We use a web-based, randomized experiment with a nationally representative sample of American adults (n = 718) to test hypotheses derived from theory and research on narrative persuasion. Respondents exposed to narratives that acknowledged individual responsibility while emphasizing obesity’s social determinants were less likely to engage in counterargument and felt more empathy for the story’s main character than those exposed to a message that did not acknowledge individual responsibility. Counterarguing and affective empathy fully mediated the relationship between message condition and support for policies to reduce rates of obesity. Failure to acknowledge individual responsibility in narratives emphasizing social determinants of obesity may undermine the persuasiveness of policy narratives. Omitting information about individual responsibility, a strongly-held American value, invites the public to engage in counterargument about the narratives and reduces feelings of empathy for a character that experiences the challenges and benefits of social determinants of obesity.

This record contains the underlying research data for the publication "Singaporean mothers' perception of their three-year-old child's weight status: A cross-sectional study" and the full-text is available from: https://ink.library.smu.edu.sg/soss_research/2459Objective: Inaccurate parental perception of their child's weight status is commonly reported in Western countries. It is unclear whether similar misperception exists in Asian populations. This study aimed to evaluate the ability of Singaporean mothers to accurately describe their three-year-old child's weight status verbally and visually.

This is the underlying dataset for the PhD dissertation: Debiasing a decision maker facing supply uncertainties in a newsvendor setting, available at: https://ink.library.smu.edu.sg/etd_coll/347/Companies must be prepared to manage uncontrollable events that will disrupt their supply chain and add uncertainty to their inventory models.This thesis first studies the effect of different types of supply disruption risks on the ordering performance of profit-maximizing decision makers in a newsvendor setting.Then, this thesis aims at extending the literature on the newsvendor model in studying the effect of a Decision Support System and the effect of a Secondary Task on the ordering performance of profit-maximizing decision makers who face supply uncertainties in a newsvendor setting.Finally, implications for scholars and practitioners are discussed.

This record contains the underlying research data for the publication "China's Innovation Landscape" and the full-text is available from: https://ink.library.smu.edu.sg/lkcsb_research/3569The People's Republic of China has experienced three decades of sustained, strong annual economic growth as it transitions from a centrally planned economy to a free market. Currently the world's second largest economy, China recognizes scientific and technological innovation as an increasingly important strategy to fuel the next phase of its productivity growth. However, the drivers and trajectories of China's scientific and technological growth remain under-investigated. To understand elements of China's innovative activities, particularly in science and technology, an analysis of comprehensive patent data provided by the State Intellectual Property Office (SIPO) of China is presented here.

This is the related dataset for the PhD dissertation "Impact of digitalization on internationalization", available at https://ink.library.smu.edu.sg/etd_coll/372/It contains Singapore company data on internationalization and digitalization, including industry classification based on companies operating charateristics and customer acquistion strategy. "Data from Impact of Digitalization on Internationalization v15" - contains the industry dataset"Data from Impact of Digitalization on Internationalization v15 - Heckman" - contains the robust test dataset"table5.xls" - contains the combined output of all tabulated data found in the thesis

This record contains the underlying research data for the publication "Disentangling greenhouse warming and aerosol cooling to reveal Earth's climate sensitivity" and the full-text is available from: https://ink.library.smu.edu.sg/soe_research/1845Earth's climate sensitivity has long been subject to heated debate and has spurred renewed interest after the latest IPCC assessment report suggested a downward adjustment of its most likely range(1). Recent observational studies have produced estimates of transient climate sensitivity, that is, the global mean surface temperature increase at the time of CO2 doubling, as low as 1.3 K (refs 2,3), well below the best estimate produced by global climate models (1.8 K). Here, we present an observation-based study of the time period 1964 to 2010, which does not rely on climate models. The method incorporates observations of greenhouse gas concentrations, temperature and radiation from approximately 1,300 surface sites into an energy balance framework. Statistical methods commonly applied to economic time series are then used to decompose observed temperature trends into components attributable to changes in greenhouse gas concentrations and surface radiation. We find that surface radiation trends, which have been largely explained by changes in atmospheric aerosol loading, caused a cooling that masked approximately one-third of the continental warming due to increasing greenhouse gas concentrations over the past half-century. In consequence, the method yields a higher transient climate sensitivity (2.0 +/- 0.8 K) than other observational studies.

Replication Data for "JUE Insight: Migration, Transportation Infrastructure, and the Spatial Transmission of COVID-19 in China"