Computer Sciences Commons^™

Historical Effects Of Electronic Interfaces, G James Mitchell

Publications and Research

Electronic interfaces are a primary tool for most professional and personal communication currently happening. Electronics, like the human mind, are limited by the understanding of executing will, or commands. This can be characterized as “interface limitations” of digital technology. Identifying this bottleneck in technological development has been critical in historical changes to both hardware and software technology. Recent medical research examines a novel user interface to reduce task load. I hypothesize, interface developments that take cues from nonverbal human communication enhance and sustain the significance of those technologies in society. By examining pivotal moments of historical technology we can identify …

Project Renew Worcester, Danni Yue, Amy Zhang, Jing Han, Omid Ashrafi, Yiming Xu

School of Professional Studies

n The client for this capstone project is RENEW Worcester which is a fledgling solar power project whose main goals are to bring renewable energy in the form of solar power into local, primarily low-income communities and are committed to the mission of making the transition off of fossil fuels to clean, renewable power. Based in Worcester, Massachusetts, they are a local chapter of Co-op Power which is a consumer-owned sustainable energy cooperative (co-op) made up of numerous different local chapters all over the New England area as well as the state of New York. The problem that we will …

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.

Social Software Development: Insights And Solutions, Abhishek Sharma

Dissertations and Theses Collection (Open Access)

Over last few decades, the way software is developed has changed drastically. From being an activity performed by developers working individually to develop standalone programs, it has transformed into a highly collaborative and cooperative activity. Software development today can be considered as a participatory culture, where developers coordinate and engage together to develop software while continuously learning from one another and creating knowledge.

In order to support their communication and collaboration needs, software developers often use a variety of social media channels. These channels help software developers to connect with like-minded developers and explore collaborations on software projects of interest. …

Comparing Elm With Svm In The Field Of Sentiment Classification Of Social Media Text Data, Zhihuan Chen, Zhaoxia Wang, Zhiping Lin, Ting Yang

Research Collection School Of Computing and Information Systems

Machine learning has been used in various fields with thousands of applications. Extreme learning machine (ELM), which is the most recently developed machine learning algorithm, has become increasingly popular for its good generalization ability. However, it has been relatively less applied to the domain of social media. Support Vector Machine (SVM), another popular learning-based algorithm, has been applied for sentiment classification of social media text data and has obtained good results. This paper investigates and compares the capabilities of these two learning-based methods in the field of sentiment classification of social media. The results indicate that SVM can obtain good …

Recommending Who To Follow In The Software Engineering Twitter Space, Abhabhisheksh Sharma, Yuan Tian, Agus Sulistya, Dinusha Wijedasa, David Lo

Research Collection School Of Computing and Information Systems

With the advent of social media, developers are increasingly using it in their software development activities. Twitter is one of the popular social mediums used by developers. A recent study by Singer et al. found that software developers use Twitter to “keep up with the fast-paced development landscape.” Unfortunately, due to the general-purpose nature of Twitter, it’s challenging for developers to use Twitter for their development activities. Our survey with 36 developers who use Twitter in their development activities highlights that developers are interested in following specialized software gurus who share relevant technical tweets.To help developers perform this task, in …

Linky: Visualizing User Identity Linkage Results For Multiple Online Social Networks (Demo), Roy Ka-Wei Lee, Ming Shan Hee, Philips Kokoh Prasetyo, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

User identity linkage across online social networks is an emerging research topic that has attracted attention in recent years. Many user identity linkage methods have been proposed so far and most of them utilize user profile, content and network information to determine if two social media accounts belong to the same person. In most cases, user identity linkage methods are evaluated by performing some prediction tasks with the results presented using some overall accuracy measures. However, the methods are rarely compared at the individual user level where a predicted matched (or linked) pair of user identities from different online social …

Improving Multi-Label Emotion Classification Via Sentiment Classification With Dual Attention Transfer Network, Jianfei Yu, Luis Marujo, Jing Jiang, Pradeep Karuturi, William Brendel

Research Collection School Of Computing and Information Systems

In this paper, we target at improving the performance of multi-label emotion classification with the help of sentiment classification. Specifically, we propose a new transfer learning architecture to divide the sentence representation into two different feature spaces, which are expected to respectively capture the general sentiment words and the other important emotion-specific words via a dual attention mechanism. Extensive experimental results demonstrate that our transfer learning approach can outperform several strong baselines and achieve the state-of-the-art performance on two benchmark datasets.

Imaginary People Representing Real Numbers: Generating Personas From Online Social Media Data, Jisun An, Haewoon Kwak, Soongyo Jung, Joni Salminen, M. Admad, Bernard J. Jansen

Research Collection School Of Computing and Information Systems

We develop a methodology to automate creating imaginary people, referred to as personas, by processing complex behavioral and demographic data of social media audiences. From a popular social media account containing more than 30 million interactions by viewers from 198 countries engaging with more than 4,200 online videos produced by a global media corporation, we demonstrate that our methodology has several novel accomplishments, including: (a) identifying distinct user behavioral segments based on the user content consumption patterns; (b) identifying impactful demographics groupings; and (c) creating rich persona descriptions by automatically adding pertinent attributes, such as names, photos, and personal characteristics. …

Building Iot Based Applications For Smart Cities: How Can Ontology Catalogs Help?, Amelia Gyrard, Antoine Zimmermann, Amit P. Sheth

Kno.e.sis Publications

The Internet of Things (IoT) plays an ever-increasing role in enabling smart city applications. An ontology-based semantic approach can help improve interoperability between a variety of IoT-generated as well as complementary data needed to drive these applications. While multiple ontology catalogs exist, using them for IoT and smart city applications require significant amount of work. In this paper, we demonstrate how can ontology catalogs be more effectively used to design and develop smart city applications? We consider four ontology catalogs that are relevant for IoT and smart cities: 1) READY4SmartCities; 2) linked open vocabulary (LOV); 3) OpenSensingCity (OSC); and 4) …

Using Electronic Health Records To Characterize Prescription Patterns: Focus On Antidepressants In Nonpsychiatric Outpatient Settings, Joseph J. Deferio, Tomer T. Levin, Judith Cukor, Samprit Banerjee, Rozan Abdulrahman, Amit P. Sheth, Neel Mehta, Jyotishman Pathak

Kno.e.sis Publications

Objective

To characterize nonpsychiatric prescription patterns of antidepressants according to drug labels and evidence assessments (on-label, evidence-based, and off-label) using structured outpatient electronic health record (EHR) data. Methods

A retrospective analysis was conducted using deidentified EHR data from an outpatient practice at a New York City-based academic medical center. Structured “medication–diagnosis” pairs for antidepressants from 35 325 patients between January 2010 and December 2015 were compared to the latest drug product labels and evidence assessments. Results

Of 140 929 antidepressant prescriptions prescribed by primary care providers (PCPs) and nonpsychiatry specialists, 69% were characterized as “on-label/evidence-based uses.” Depression diagnoses were associated …

Poster: Privacy-Preserving Boosting With Random Linear Classifiers, Sagar Sharma, Keke Chen

Kno.e.sis Publications

We propose SecureBoost, a privacy-preserving predictive modeling framework, that allows service providers (SPs) to build powerful boosting models over encrypted or randomly masked user submit- ted data. SecureBoost uses random linear classifiers (RLCs) as the base classifiers. A Cryptographic Service Provider (CSP) manages keys and assists the SP’s processing to reduce the complexity of the protocol constructions. The SP learns only the base models (i.e., RLCs) and the CSP learns only the weights of the base models and a limited leakage function. This separated parameter holding avoids any party from abusing the final model or conducting model-based attacks. We evaluate …

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 …

Implicit Linking Of Food Entities In Social Media, Wen Haw Chong, Ee Peng Lim

Research Collection School Of Computing and Information Systems

Dining is an important part in people’s lives and this explains why food-related microblogs and reviews are popular in social media. Identifying food entities in food-related posts is important to food lover profiling and food (or restaurant) recommendations. In this work, we conduct Implicit Entity Linking (IEL) to link food-related posts to food entities in a knowledge base. In IEL, we link posts even if they do not contain explicit entity mentions. We first show empirically that food venues are entity-focused and associated with a limited number of food entities each. Hence same-venue posts are likely to share common food …

Diversity In Online Advertising: A Case Study Of 69 Brands On Social Media, Jisun An, Ingmar Weber

Research Collection School Of Computing and Information Systems

Lack of diversity in advertising is a long-standing problem. Despite growing cultural awareness and missed business opportunities, many minorities remain under- or inappropriately represented in advertising. Previous research has studied how people react to culturally embedded ads, but such work focused mostly on print media or television using lab experiments. In this work, we look at diversity in content posted by 69 U.S. brands on two social media platforms, Instagram and Facebook. Using face detection technology, we infer the gender, race, and age of both the faces in the ads and of the users engaging with ads. Using this dataset, …

Architecture Of Aggression In Cyberspace. Testing Cyber Aggression In Young Adults In Hungary, Katalin Parti, Tibor Kiss, Gergely Koplányi

International Journal of Cybersecurity Intelligence & Cybercrime

In order to test whether and how violence is exacerbated in online social networking sites, we utilized the BryantSmith Aggression Scale (Bryant & Smith, 2001), and included examples in the questionnaire offering solutions for 7 different hypothetical cases occurring online (Kiss, 2017). The questionnaire was sent to social work and law school students in Hungary. Prevalence and levels of aggression and its manifestation as violence online proved to be not more severe than in offline social relations. Law students were more aware than students of social work that online hostile acts are discrediting. Students of social work were significantly more …

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.

Creating Real-Time Dynamic Knowledge Graphs, Swati Padhee, Sarasi Lalithsena, Amit P. Sheth

Kno.e.sis Publications

No abstract provided.

Context Recovery In Location-Based Social Networks, Wen Haw Chong

Dissertations and Theses Collection (Open Access)

This dissertation addresses context recovery in Location-Based Social Networks (LBSN), which are platforms where users post content from various locations. With this general LBSN definition, many existing social media platforms that support user-generated location relevant content using mobile devices could also qualify as LBSNs. Context recovery for such user posts refers to recovering the venue and the semantic contexts of these user posts. Such information is useful for user profiling and to support various applications such as venue recommendation and location- based advertising.

The Silencing Power Of Algorithms: How The Facebook News Feed Algorithm Manipulates Users' Perceptions Of Opinion Climates, Callie Jessica Morgan

University Honors Theses

This extended literature review investigates how the architecture and features of the Facebook Newsfeed algorithm, EdgeRank, can inhibit and facilitate the expression of political opinions. This paper will investigate how Elisabeth Noelle-Neumann's theory on public opinion, Spiral of Silence, can be used to assess the Facebook news feed as a political opinion source that actively shapes users' perceptions of minority and majority opinion climates. The feedback loops created by the algorithm's criteria influences users' decisions to self-censor or express their political opinions with interpersonal connections and unfamiliar connections on the site.

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 …

Role Of Social Media In Public Accounting Firms, Brenda Eschenbrenner, Fiona Fui-Hoon Nah, Zhiwei Lu

Research Collection School Of Computing and Information Systems

Social media has been widely used for both professional and personal communications. Businesses recognize the importance of social media and are using them to fulfill various business objectives. In this paper, we focus on analyzing the business objectives of public accounting firms that have both a firm-wide main page and a career page on Facebook. More specifically, we compare the business objectives they are achieving with their firm-wide main pages versus career pages. We not only find differences in the objectives that are being achieved, but also identify other objectives that are not actively being pursued on either page but …

Pacela: A Neural Framework For User Visitation In Location-Based Social Networks, Thanh Nam Doan, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Check-in prediction using location-based social network data is an important research problem for both academia and industry since an accurate check-in predictive model is useful to many applications, e.g. urban planning, venue recommendation, route suggestion, and context-aware advertising. Intuitively, when considering venues to visit, users may rely on their past observed visit histories as well as some latent attributes associated with the venues. In this paper, we therefore propose a check-in prediction model based on a neural framework called Preference and Context Embeddings with Latent Attributes (PACELA). PACELA learns the embeddings space for the user and venue data as well …

Augmented Personalized Health: Using Semantically Integrated Multimodal Data For Patient Empowered Health Management Strategies, Amit P. Sheth, Hong Y. Yip, Utkarshani Jaimini, Dipesh Kadariya, Vaikunth Sridharan, R. Venkataramanan, Tanvi Banerjee, Krishnaprasad Thirunarayan, Maninder Kalra

Kno.e.sis Publications

Healthcare as we know it is in the process of going through a massive change from:

1. Episodic to continuous

2. Disease-focused to wellness and quality of life focused

3. Clinic-centric to anywhere a patient is

4. Clinician controlled to patient empowered

5. Being driven by limited data to 360-degree, multimodal personal-public-population physical-cyber-social big data-driven URL: https://mhealth.md2k.org/2018-tech-showcase-home

From 2,772 Segments To Five Personas: Summarizing A Diverse Online Audience By Generating Culturally Adapted Personas, Joni Salminen, Sercan Sengun, Haewoon Kwak, Bernard J. Jansen, Jisun An, Soon-Gyu Jung, Sarah Vieweg, D. Fox Harrell

Research Collection School Of Computing and Information Systems

Understanding users in the era of social media is challenging, requiring organizations to adopt novel computation-aided approaches. To exemplify such an approach, we retrieved information on millions of interactions with YouTube video content from a major Middle Eastern media outlet, to automatically generate personas that capture how different audience segments interact with thousands of individual content pieces. Then, we used qualitative data to provide additional insights into the automatically generated persona profiles. Our findings provide insights into social media usage in the Middle East and demonstrate the application of a novel methodology that generates culturally adapted personas of social media …

Temporal And Spatiotemporal Investigation Of Tourist Attraction Visit Sentiment On Twitter, Jose J. Padilla, Hamdi Kavak, Christopher J. Lynch, Ross J. Gore, Saikou Y. Diallo

VMASC Publications

In this paper, we propose a sentiment-based approach to investigate the temporal and spatiotemporal effects on tourists' emotions when visiting a city's tourist destinations. Our approach consists of four steps: data collection and preprocessing from social media; visitor origin identification; visit sentiment identification; and temporal and spatiotemporal analysis. The temporal and spatiotemporal dimensions include day of the year, season of the year, day of the week, location sentiment progression, enjoyment measure, and multi-location sentiment progression. We apply this approach to the city of Chicago using over eight million tweets. Results show that seasonal weather, as well as special days and …

Automatically Conceptualizing Social Media Analytics Data Via Personas, Jung S.G., Salminen J., An J., Kwak H., Jansen B.J.

Research Collection School Of Computing and Information Systems

Social media analytics is insightful but can also be difficult to use within organizations. To address this, we present Automatic Persona Generation (APG), a system and methodology for quantitatively generating personas using large amounts of online social media data. The APG system is operational, deployed in a pilot version with several organizations in multiple industry verticals. APG uses a robust web and stable back-end database framework to process tens of millions of user interactions with thousands of online digital products on multiple social media platforms, including Facebook and YouTube. APG identifies both distinct and impactful audience segments for an organization …

Epoca House: The Implementation Strategy, Courtney Thomas, Yaffa Fain, Shuxiao Teng, Mingjun Xie

School of Professional Studies

This capstone report provides a written strategic implementation plan for EPOCA House. EPOCA, Ex-Prisoners and Prisoners Organizing for Community Advancement, is a non-profit dedicated to creating better resources and opportunities for prisoners and ex-prisoners in Worcester. The organization’s most recent initiative, EPOCA House, is a transitional facility that will provide reentry services and temporary housing to ex-offenders in and around the Worcester area. Recently, Massachusetts passed a comprehensive criminal justice reform bill that will dramatically reduce the number of people being incarcerated; however, little attention is focused on what will happen to individuals after they leave prison. At the same …

A Guide For Hiring Mature Employees At Ascentria, Binghe Change, Calleigh Leach, Elsie Lu, Hongquiang You, Mengge Wang, Nina Mariotti

School of Professional Studies

This paper will explore existing literature on aspects of an intergenerational workplace in a nonprofit organization and provide recommendations for recruiting and retaining older employees for a human service organization, Ascentria Care Alliance, in Worcester, MA. The paper will review research on trends in the nonprofit industry, a comprehensive breakdown of generational workplace values, reasons people are returning to work and effective marketing strategies that appeal to older generations. Marketing strategies specifically will explore best practices for website design, social media usage, and job descriptions. Additionally, this paper will research case studies of nonprofits with successful intergenerational workforce initiatives, as …