Numerical Analysis and Scientific Computing Commons^™

Online Burst Events Detection Oriented Real-Time Microblog Message Stream, Guozhong Dong, Jun Gao, Liang Huang, Chunlei Shi

Research Collection School Of Computing and Information Systems

The rapid spread of microblog messages and sensitivity of unexpected events make microblog become the public opinion center of burst events. Online burst events detection oriented real-time microblog message stream has become an important research problem in the field of microblog public opinion. Because of the large amount of real-time microblog message stream and irregular language of microblog message, it is important to process real-time microblog message stream and detect burst events accurately. In this paper, an online burst events detection framework is proposed. In this framework, abnormal messages are detected based on sliding time window and two-level hash table. …

Data Mining Approach To The Detection Of Suicide In Social Media: A Case Study Of Singapore, Jane H. K. Seah, Kyong Jin Shim

Research Collection School Of Computing and Information Systems

In this research, we focus on the social phenomenon of suicide. Specifically, we perform social sensing on digital traces obtained from Reddit. We analyze the posts and comments in that are related to depression and suicide. We perform natural language processing to better understand different aspects of human life that relate to suicide.

Building Classifiers With Gmdh For Health Social Networks (Bd Askapatient), John Cardiff, Liliya Akhtyamova, Mikhail Alexandrov

Conference Papers

Health social media offer useful data for patients and doctors concerning both various medicines and treatments. Usually, these data are accompanied by their assessments in 5- star scale. But such a detail classification has small usefulness because patients and doctors, first of all, want to know about negative cases and to study in detail the extreme ones. In the paper we build classifiers of texts just for these cases using combined classes as negative, all others and worst, satisfactory, best. For this, we study possibilities of different GMDH-based algorithms and compare them with the results of other methods. The selection …

Theatrical Genre Prediction Using Social Network Metrics, Manisha Shukla

Graduate Theses and Dissertations

With the emergence of digitization, large text corpora are now available online that provide humanities scholars an opportunity to perform literary analysis leveraging the use of computational techniques. This work is focused on applying network theory concepts in the field of literature to explore correlations between the mathematical properties of the social networks of plays and the plays’ dramatic genre, specifically how well social network metrics can identify genre without taking vocabulary into consideration. Almost no work has been done to study the ability of mathematical properties of network graphs to predict literary features. We generated character interaction networks of …

Offline Versus Online: A Meaningful Categorization Of Ties For Retweets, Felicia Natali, Feida Zhu

Research Collection School Of Computing and Information Systems

With the recent proliferation of news being shared through online social networks, it is crucial to determine how news is spread and what drives people to share certain stories. In this paper, we focus on the social networking site Twitter and analyse user’s retweets. We study retweeting patterns between offline and online friends, particularly, how tweet novelty and tweet topic differ between tweets retweeted by offline friends and those retweeted by online friends.

Detecting Personal Intake Of Medicine From Twitter, Debanjan Mahata, Jasper Friedrichs, Rajiv Ratn Shah, Jing Jiang

Research Collection School Of Computing and Information Systems

Mining social media messages such as tweets, blogs, and Facebook posts for health and drug related information has received significant interest in pharmacovigilance research. Social media sites (e.g., Twitter), have been used for monitoring drug abuse, adverse reactions to drug usage, and analyzing expression of sentiments related to drugs. Most of these studies are based on aggregated results from a large population rather than specific sets of individuals. In order to conduct studies at an individual level or specific groups of people, identifying posts mentioning intake of medicine by the user is necessary. Toward this objective we develop a classifier …

Discovering Hidden Topical Hubs And Authorities In Online Social Networks, Roy Ka-Wei Lee, Tuan-Anh Hoang, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Finding influential users in online social networks is an important problem with many possible useful applications. HITS and other link analysis methods, in particular, have been often used to identify hub and authority users in web graphs and online social networks. These works, however, have not considered topical aspect of links in their analysis. A straightforward approach to overcome this limitation is to first apply topic models to learn the user topics before applying the HITS algorithm. In this paper, we instead propose a novel topic model known as Hub and Authority Topic (HAT) model to combines the two process …

Exploiting User And Venue Characteristics For Fine-Grained Tweet Geolocation, Wen Haw Chong, Ee Peng Lim

Research Collection School Of Computing and Information Systems

Which venue is a tweet posted from? We call this a fine-grained geolocation problem. Given an observed tweet, the task is to infer its discrete posting venue, e.g., a specific restaurant. This recovers the venue context and differs from prior work, which geolocats tweets to location coordinates or cities/neighborhoods. First, we conduct empirical analysis to uncover venue and user characteristics for improving geolocation. For venues, we observe spatial homophily, in which venues near each other have more similar tweet content (i.e., text representations) compared to venues further apart. For users, we observe that they are spatially focused and more likely …

What Is Gab: A Bastion Of Free Speech Or An Alt-Right Echo Chamber, Savvas Zannettou, Barry Bradlyn, Emiliano De Cristofaro, Haewoon Kwak, Michael Sirivianos, Gianluca Stringhini, Jeremy Blackburn

Research Collection School Of Computing and Information Systems

Over the past few years, a number of new "fringe" communities, like 4chan or certain subreddits, have gained traction on the Web at a rapid pace. However, more often than not, little is known about how they evolve or what kind of activities they attract, despite recent research has shown that they influence how false information reaches mainstream communities. This motivates the need to monitor these communities and analyze their impact on the Web's information ecosystem. In August 2016, a new social network called Gab was created as an alternative to Twitter. It positions itself as putting "people and free …

Multilingual Sentiment Analysis : From Formal To Informal And Scarce Resource Languages, Siaw Ling Lo, Erik Cambria, Raymond Chiong, David Cornforth

Research Collection School Of Computing and Information Systems

The ability to analyse online user-generated content related to sentiments (e.g., thoughts and opinions) on products or policies has become a de-facto skillset for many companies and organisations. Besides the challenge of understanding formal textual content, it is also necessary to take into consideration the informal and mixed linguistic nature of online social media languages, which are often coupled with localised slang as a way to express ‘true’ feelings. Due to the multilingual nature of social media data, analysis based on a single official language may carry the risk of not capturing the overall sentiment of online content. While efforts …

Inferring Social Media Users’ Demographics From Profile Pictures: A Face++ Analysis On Twitter Users, Soon-Gyo Jung, Jisun An, Haewoon Kwak, Joni Salminen, Bernard J. Jansen

Research Collection School Of Computing and Information Systems

In this research, we evaluate the applicability of using facial recognition of social media account profile pictures to infer the demographic attributes of gender, race, and age of the account owners leveraging a commercial and well-known image service, specifically Face++. Our goal is to determine the feasibility of this approach for actual system implementation. Using a dataset of approximately 10,000 Twitter profile pictures, we use Face++ to classify this set of images for gender, race, and age. We determine that about 30% of these profile pictures contain identifiable images of people using the current state-of-the-art automated means. We then employ …

Systematic Analysis Of Enterprise Perception Towards Cloud Adoption In The African States: The Nigerian Perspective, George A. Oguntala, Prof. Raed A. Abd-Alhameed, Dr. Janet O. Odeyemi

The African Journal of Information Systems

The desirous benefits of cloud computing such as high return on investment through efficient resource management, high application throughput and on-demand capabilities have resulted in the unprecedented global acceptance of the computing paradigm. However, research on cloud adoption indicates that fewer organisations in the African states are adopting cloud services. Thus, the purview of the paper is to examine the factors responsible for the poor adoption of cloud computing in most African enterprises using Nigeria as a case study. The study focus on the perception of IT and non-IT employees towards cloud computing. Moreover, the paper reviews the literature on …

Generating Cultural Personas From Social Data: A Perspective Of Middle Eastern Users, Salminen Joni, Sercan Sengün, Haewoon Kwak, Bernard Jansen, Jisun An, Soon-Gyo Jung, Sarah Vieweg, D. Fox Harrell

Research Collection School Of Computing and Information Systems

We conduct a mixed-method study to better understand the content consumption patterns of Middle Eastern social media users and to explore new ways to present online data by using automatic persona generation. First, we analyze millions of content interactions on YouTube to dynamically generate personas describing behavioral patterns of different demographic groups. Second, we analyze interview data on social media users in the Middle Eastern region to generate additional insights into the dynamically generated personas. Our findings provide insights into social media users in the Middle East, as well as present a novel methodology of using computational analysis and qualitative …

Demographics Of News Sharing In The U.S. Twittersphere, Julio C.S. Reis, Haewoon Kwak, Jisun An, Johnnatan Messias, Benevenuto Fabrıcio.

Research Collection School Of Computing and Information Systems

The widespread adoption and dissemination of online news through social media systems have been revolutionizing many segments of our society and ultimately our daily lives. In these systems, users can play a central role as they share content to their friends. Despite that, little is known about news spreaders in social media. In this paper, we provide the first of its kind in-depth characterization of news spreaders in social media. In particular, we investigate their demographics, what kind of content they share, and the audience they reach. Among our main findings, we show that males and white users tend to …

A Domain Based Approach To Social Relation Recognition, Qianru Sun, Bernt Schiele, Mario Fritz

Research Collection School Of Computing and Information Systems

Social relations are the foundation of human daily life. Developing techniques to analyze such relations from visual data bears great potential to build machines that better understand us and are capable of interacting with us at a social level. Previous investigations have remained partial due to the overwhelming diversity and complexity of the topic and consequently have only focused on a handful of social relations. In this paper, we argue that the domain-based theory from social psychology is a great starting point to systematically approach this problem. The theory provides coverage of all aspects of social relations and equally is …

Persona Generation From Aggregated Social Media Data, Soon-Gyo Jung, Jisun An, Haewoon Kwak, Moeed Ahmad, Lene Nielsen, Bernard J. Jansen

Research Collection School Of Computing and Information Systems

We develop a methodology for persona generation using real time social media data for the distribution of products via online platforms. From a large social media account containing more than 30 million interactions from users from 181 countries engaging with more than 4,200 digital products produced by a global media corporation, we demonstrate that our methodology can first identify both distinct and impactful user segments and then create persona descriptions by automatically adding pertinent features, such as names, photos, and personal attributes. We validate our approach by implementing the methodology into an actual working system that leverages large scale online …

Modeling Topics And Behavior Of Microbloggers: An Integrated Approach, Tuan Anh Hoang, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Microblogging encompasses both user-generated content and behavior. When modeling microblogging data, one has to consider personal and background topics, as well as how these topics generate the observed content and behavior. In this article, we propose the Generalized Behavior-Topic (GBT) model for simultaneously modeling background topics and users' topical interest in microblogging data. GBT considers multiple topical communities (or realms) with different background topical interests while learning the personal topics of each user and the user's dependence on realms to generate both content and behavior. This differentiates GBT from other previous works that consider either one realm only or content …

Inferring User Consumption Preferences From Social Media, Yang Li, Jing Jiang, Ting Liu

Research Collection School Of Computing and Information Systems

Social Media has already become a new arena of our lives and involved different aspects of our social presence. Users' personal information and activities on social media presumably reveal their personal interests, which offer great opportunities for many e-commerce applications. In this paper, we propose a principled latent variable model to infer user consumption preferences at the category level (e.g. inferring what categories of products a user would like to buy). Our model naturally links users' published content and following relations on microblogs with their consumption behaviors on e-commerce websites. Experimental results show our model outperforms the state-of-the-art methods significantly …

Discovering Burst Patterns Of Burst Topic In Twitter, Guozhong Dong, Wu Yang, Feida Zhu, Wei Wang

Research Collection School Of Computing and Information Systems

Twitter has become one of largest social networks for users to broadcast burst topics. There have been many studies on how to detect burst topics. However, mining burst patterns in burst topics has not been solved by the existing works. In this paper, we investigate the problem of mining burst patterns of burst topic in Twitter. A burst topic user graph model is proposed, which can represent the topology structure of burst topic propagation across a large number of Twitter users. Based on the model, hierarchical clustering is applied to cluster burst topics and reveal burst patterns from the macro …

Fine-Grained Sentiment Analysis Of Social Media With Emotion Sensing, Zhaoxia Wang, Chee Seng Chong, Landy Lan, Yinping Yang, Beng-Seng Ho, Joo Chuan Tong

Research Collection School Of Computing and Information Systems

Social media is arguably the richest source of human generated text input. Opinions, feedbacks and critiques provided by internet users reflect attitudes and sentiments towards certain topics, products, or services. The sheer volume of such information makes it effectively impossible for any group of persons to read through. Thus, social media sentiment analysis has become an important area of work to make sense of the social media talk. However, most existing sentiment analysis techniques focus only on the aggregate level, classifying sentiments broadly into positive, neutral or negative, and lack the capabilities to perform fine-grained sentiment analysis. This paper describes …

Domain Identification For Intention Posts On Online Social Media, Thai Le Luong, Quoc Tuan Truong, Hai-Trieu Dang, Xuan Hieu Phan

Research Collection School Of Computing and Information Systems

Today, more and more Internet users are willing to share their feeling, activities, and even their intention about what they plan to do on online social media. We can easily see posts like "I plan to buy an apartment this year", or "We are looking for a tour for 3 people to Nha Trang" on online forums or social networks. Recognizing those user intents on online social media is really useful for targeted advertising. However fully understanding user intents is a complicated and challenging process which includes three major stages: user intent filtering, intent domain identification, and intent parsing and …

On Profiling Bots In Social Media, Richard J. Oentaryo, Arinto Murdopo, Philips K. Prasetyo, Ee Peng Lim

Research Collection School Of Computing and Information Systems

The popularity of social media platforms such as Twitter has led to the proliferation of automated bots, creating both opportunities and challenges in information dissemination, user engagements, and quality of services. Past works on profiling bots had been focused largely on malicious bots, with the assumption that these bots should be removed. In this work, however, we find many bots that are benign, and propose a new, broader categorization of bots based on their behaviors. This includes broadcast, consumption, and spam bots. To facilitate comprehensive analyses of bots and how they compare to human accounts, we develop a systematic profiling …

Context-Aware Advertisement Recommendation For High-Speed Social News Feeding, Yuchen Li, Dongxiang Zhang, Ziquan Lan, Kian-Lee Tan

Research Collection School Of Computing and Information Systems

Social media advertising is a multi-billion dollar market and has become the major revenue source for Facebook and Twitter. To deliver ads to potentially interested users, these social network platforms learn a prediction model for each user based on their personal interests. However, as user interests often evolve slowly, the user may end up receiving repetitive ads. In this paper, we propose a context-aware advertising framework that takes into account the relatively static personal interests as well as the dynamic news feed from friends to drive growth in the ad click-through rate. To meet the real-time requirement, we first propose …

Mining And Clustering Mobility Evolution Patterns From Social Media For Urban Informatics, Chien-Cheng Chen, Meng-Fen Chiang, Wen-Chih Peng

Research Collection School Of Computing and Information Systems

In this paper, given a set of check-in data, we aim at discovering representative daily movement behavior of users in a city. For example, daily movement behavior on a weekday may show users moving from one to another spatial region associated with time information. Since check-in data contain both spatial and temporal information, we propose a mobility evolution pattern to capture the daily movement behavior of users in a city. Furthermore, given a set of daily mobility evolution patterns, we formulate their similarity distances and then discover representative mobility evolution patterns via the clustering process. Representative mobility evolution patterns are …

#Greysanatomy Vs. #Yankees: Demographics And Hashtag Use On Twitter, Jisun An, Ingmar Weber

Research Collection School Of Computing and Information Systems

Demographics, in particular, gender, age, and race, are a key predictor of human behavior. Despite the significant effect that demographics plays, most scientific studies using online social media do not consider this factor, mainly due to the lack of such information. In this work, we use state-of-the-art face analysis software to infer gender, age, and race from profile images of 350K Twitter users from New York. For the period from November 1, 2014 to October 31, 2015, we study which hashtags are used by different demographic groups. Though we find considerable overlap for the most popular hashtags, there are also …

Using Digital Genomics To Create An Intelligent Enterprise, Mario Domingo

Asian Management Insights

Every business knows that it needs to leverage customer data, but few know the potential it has to transform business processes, decisions and performance.

Whom Should We Sense In 'Social Sensing' - Analyzing Which Users Work Best For Social Media Now-Casting, Jisun An, Ingmar Weber

Research Collection School Of Computing and Information Systems

Given the ever increasing amount of publicly available social media data, there is growing interest in using online data to study and quantify phenomena in the offline 'real' world. As social media data can be obtained in near real-time and at low cost, it is often used for 'now-casting' indices such as levels of flu activity or unemployment. The term 'social sensing' is often used in this context to describe the idea that users act as 'sensors', publicly reporting their health status or job losses. Sensor activity during a time period is then typically aggregated in a 'one tweet, one …

A Method And System For Sentiment Classification And Emotion Classification [Us Patent 20170308523a1], Zhaoxia Wang, Rick Siow Mong Goh, Yinping Yang

Research Collection School Of Computing and Information Systems

A system and a method for classifying text messages, such as social media messages into sentiment valence categories are provided. The system comprising a module for decomposing text messages, a module for cleaning text messages, a module for producing feature data of text messages, and a module for classifying text messages into sentiment valence categories. The module for decomposing text messages is configured to: receive a text message, parse the text message into separate portions in response to parsing criteria based on sentence delimiters, wherein the separate portions are sentences, phrases and words, and rejoin at least some of the …

Structured Learning From Heterogeneous Behavior For Social Identity Linkage, Siyuan Liu, Shuhui Wang, Feida Zhu

Research Collection School Of Computing and Information Systems

Social identity linkage across different social media platforms is of critical importance to business intelligence by gaining from social data a deeper understanding and more accurate profiling of users. In this paper, we propose a solution framework, HYDRA, which consists of three key steps: (I) we model heterogeneous behavior by long-term topical distribution analysis and multi-resolution temporal behavior matching against high noise and information missing, and the behavior similarity are described by multi-dimensional similarity vector for each user pair; (II) we build structure consistency models to maximize the structure and behavior consistency on users' core social structure across different platforms, …

Should We Use The Sample? Analyzing Datasets Sampled From Twitter's Stream Api, Yazhe Wang, Jamie Callan, Baihua Zheng

Research Collection School Of Computing and Information Systems

Researchers have begun studying content obtained from microblogging services such as Twitter to address a variety of technological, social, and commercial research questions. The large number of Twitter users and even larger volume of tweets often make it impractical to collect and maintain a complete record of activity; therefore, most research and some commercial software applications rely on samples, often relatively small samples, of Twitter data. For the most part, sample sizes have been based on availability and practical considerations. Relatively little attention has been paid to how well these samples represent the underlying stream of Twitter data. To fill …