Doctor of Philosophy (Ph.D.)

Permanent URI for this collectionhttp://192.248.9.226/handle/123/12348

Browse

Now showing 1 - 3 of 3

item: Thesis-Full-text
A cross platform framework for social media information diffusion analysis
(2023) Caldera, HMM; Perera, GIUS
In the current digital era, social media platforms have emerged as one of the most effec- tive channels for the diffusion of information. People may readily access and exchange information, news, and opinions from anywhere worldwide because of increasing social media usage. Information diffusion across multiplex social media platforms is one of the most prominent research problems ever. Social media content generators diffuse information on multiplex social media platforms by targeting many objectives such as popularity, online presence, hate targets, and customer engagement. Regardless of the ”content” posted on social media platforms, evaluating the dissemination velocity of each piece of content published on those platforms is essential. It will help to get an overall picture of ”how it flows” throughout the social media platforms. Most social media platforms have a platform-specific algorithm for calculating the degree of information diffusion on those platforms. The main objective of this research was to develop a method to calculate the velocity of information diffusion across multiplex social media platforms. Existing literature on information diffusion strategies, effects, and measurements was used to develop the proposed algorithm. The information diffusion velocity of so- cial media influencers varies according to the content. The platform-specific algorithms for diffusion strength detection vary based on the platform. Somehow, these platform- specific algorithms influence the community to engage with the trending content. i.e., platforms support increasing the strength of information diffusion. Conventional information diffusion algorithms were designed to measure content diffusion speed on a simplex social media platform, which might be content-specific. The missing dimension is ubiquitous nature. Hence, regardless of the platform, it is mandatory to calculate a ubiquitous information diffusion velocity over multiplex social media platforms. Both structured information diffusion in a graph for diffusion in a closed network and unstructured patterns in an open-ended coarse-grained information diffusion model check the importance of information diffusion on multiplex social media platforms. Time is another critical factor in defining velocity. i.e., a time series of information diffusion provides a rich picture of information diffusion. Event-driven architecture is a well-known software architectural approach that fa- cilitates the implementation of microservice-based solutions. The suggested algorithm utilizes an event-driven architecture to manage the information flow by processing social media events. Eventually, this research uses the event-triggering process to understand how information is propagated through an event-driven microservice architecture. Data science and artificial intelligence are being employed in information diffusion studies. Understanding how information spreads and the variables and features that influence it is another crucial study area of this research. There are several techniques for studying information dissemination using artificial intelligence. Applying artificial intelligence to information diffusion studies might improve our knowledge of ”How information travels” and ”how to disseminate information” in various circumstances efficiently. The research used natural language processing to evaluate the textual content of the social media post. That is to find a general textual meaning given by the end-user reactions. Event-driven architecture is one of the best possible for information diffusion an- alytics. Using event-driven architecture, data may be delivered in real-time to vari- ous analytics services, allowing for the speedy and effective processing of enormous amounts of data. This is especially true in today’s data-driven world, when businesses and organizations must make quick, well-informed decisions based on real-time data. Because of its event-driven nature, it is also simple to interface with other systems and services, making it a highly adaptable and versatile option for information distribution analytics. Since the diffusion of information starts with an event’s occurrence, it fol- lows numerous steps to flow among the community. An event-driven micro-services architecture that uses artificial intelligence methods (like natural language processing to evaluate textual information) has been experimented with to propose a simple solution for this complex problem. As per the research work, I can summarize the key findings. I have proposed a tree-structured diffusion tree that can explain how information flows through multiplex social networks. Under this multiplex context, I have experimented with multiple trees and a more robust graph that focused on the diffusion of information. The diffusion strength was based on the SIR model, and the time series analysis focused on how quickly information spread throughout the network. The proposed solution was tested in several real-world cases. Technique-specific tests like seasonality and autocorrelation were conducted to evaluate how the time-series model works in a graph context. Further tests like cohesiveness and robustness were tested, and the proposed algorithm achieved good robustness (an average of 75%) and cohesiveness (an average of 70%) in each case. The best experimental results show an average of more than 80% accuracy in any given instance, and it constructs the tree in less than a second. Most of the predicted values generated an average accuracy of around 70%. In summary, social media platforms have emerged as prominent channels for in- formation propagation within the contemporary digital landscape. Quantifying the velocity at which information propagates across diverse social networks presents a no- table challenge in research. While algorithms tailored to specific platforms influence community engagement, a ”universal metric for information dissemination strength” is necessary across multiple social media platforms. The envisioned algorithm considers time series data, integrating structured and unstructured patterns during construction. Keywords: Information diffusion analysis, Social Media Data Analytics, Graph Learning, Time series analysis, Event-driven micro-services, Artificial Intelligence, Natural Language Processing.
item: Thesis-Abstract
Improving the effectiveness of MOOCs to meet the 21st century challenges
(2021) Gamage SD; Fernando MSD; Perera GIUS
Massive Open Online Courses (MOOCs) are a type of online course designed using principles of education technology. It enables a massive number of participants to learn online in any course at any time. This affordance of scaling and open access to education is considered as the globalized solution for acquiring 21st century skills. However, unrealistic to the vision, pragmatically, MOOCs are facing challenges. Mainly the content-driven pedagogical structure with limited system design implications caused fewer interactions and isolations, thereby resulted in higher dropouts. Since MOOCs are introduced recently, the problems faced by participants or its effectiveness are less understood. Thus, a systematic understanding of arising problems and solutions to this newly emerged phenomenon is well needed. In this thesis, I explored MOOCs with a holistic view of understanding emerging problems with empirical pieces of evidence—whether MOOCs meet the 21st century skill requirements; what factors are affecting the effectiveness of a MOOC; how can we improve the effectiveness of MOOCs. By exploring the above questions, this thesis mainly contributes to 1) provide empirical evidence of the challenges that MOOCs are facing, 2) solicit a framework to identify the effectiveness of MOOCs, 3) design a novel peer review mechanism, and 4) develop the novel system PeerCollab to improve effectiveness of MOOCs. The research begun with exploratory research methods with active data collection using MOOC users. The analysis conducted using a combined approach of qualitative and quantitative methods to understand the challenges and explore the factors affecting the effectiveness of MOOCs. Initially, surveys were used to identify whether MOOC platforms are providing necessary 21st century skills such as collaborative skills, creativity skills, communications skills, and critical thinking skills. Next, a longitudinal qualitative study was used to gather MOOC experience using participants over 24 months period of time. Results of the qualitative study were incorporated to build an instrument to evaluate MOOCs' effectiveness. The instrument was empirically verified and validated using 121 MOOC participants. The initial survey to explore 21st century skills yielded results from 391 MOOC participants across six platforms. Descriptive statistics depicted that majority of participants reflect the gap in MOOCs to provide 21st century skills. Next, the qualitative analysis using Grounded Theory (GT) and quantitative analysis using Factor Analysis (FA) resulted in a detailed10-dimensional framework to evaluate MOOC effectiveness. Based on the high ranked dimensions in the framework such as Technology, Collaborativeness, Interactivity and Assessment, two systems were designed and developed to demonstrate the improved effectiveness in MOOCs. First, the “Identified Peer Review” (IPR) system demonstrated how peer identity, incentive algorithm, and effective communication in peer review enhance the MOOC's effectiveness. Next, the PeerCollab system demonstrated how social presence can integrate using theories of communities of practices (CoP) into MOOCs and thereby improve effectiveness. This system also demonstrated an articulation of CoP to MOOCs by a novel process named Rapid Communities on MOOCs (RCoM) design with four phases, viz. Cluster, Orient, Focus, and iii Network. Evaluations of the systems demonstrated the challenges and possibilities of integrating such systems into MOOCs and provided a direction to build effective interventions. These systems collectively empower interactions in isolated distributed individuals and form communities to work collectively bridging the gap to meet the 21st century skills. The work of this thesis actively contributes to the nuance of technologies that can be used in society specifically for large scale open and distributed learning contexts.
item: Thesis-Full-text
Measuring trustworthiness of workers in the crowdsourced collection of subjective judgements
(2023) Meedin, GSN; Perera, GIUS
Social media platforms have become integral parts of our lives, enabling people to connect, share, and express themselves on a global scale. Alongside the benefits, there are also substantial challenges that arise from the unfiltered and unrestricted nature of these platforms. One such challenge is the presence of inappropriate and hateful content on social media. While platforms employ algorithms and human moderators to identify and remove inappropriate content, they often struggle to keep up with the constant flood of new posts. Social media posts are written in a variety of languages and multimedia formats. As a result, social media platforms find it more difficult to filter these before reaching a more diverse audience range, as moderation of these social media platform posts necessitates greater contextual, social, and cultural insights, as well as language skills. Social media platforms use a variety of techniques to capture these insights, and linguistic expertise to effectively moderate social media posts. These techniques help platforms better understand the degrees of content and ensure that inappropriate or harmful posts are accurately identified and addressed. These techniques include Natural Language Processing (NLP) algorithms, keyword and phrase detection, image and video recognition, contextual analysis, cultural sensitivity training, machine learning, AI improvement etc. Data annotation forms the foundation for training these algorithms and identifying and classifying various types of content accurately. Often crowdsourcing platforms such as Mechanical Turk and Crowd Flower are used to get the datasets annotated in these techniques. The accuracy of the annotation process is crucial for effective content moderation on social media platforms. Crowdsourcing platforms take several trust measures to maintain the quality of annotations and to minimize errors. In addition to these procedures, determining the trustworthiness of workers on crowdsourcing platforms is critical for ensuring the quality and reliability of the contributions they give. Accuracy metrics, majority voting, completion rate, inter-rater agreement, and reputation scores are a few such measurements used by existing researchers. Even though majority voting is used to ensure consensus, existing research shows that the annotated results do not reflect the actual user perception and hence the trustworthiness of the annotation is less. In this research, a crowdsourcing platform was designed and developed to allow the annotation process by overcoming the limitations of measuring trustworthiness which would facilitate identifying inappropriate social media content using crowd responses. Here the research focus was limited to social media content written in Sinhala and Sinhala words written in English (Singlish) letters as the most popular Mechanical Turk and Crowd Flower do not allow workers from Sri Lanka. As outcomes of this research, a few novel approaches were proposed, implemented, and evaluated for hate speech annotation, hate speech corpus generation, measuring user experience, identifying worker types and personality traits and hate speech post-identification. In addition, the implemented crowdsourcing platform can extend the task designs to other annotation tasks; language and inappropriate content identification, text identification from images, hate speech propagator ranking and sentiment analysis. When evaluating the quality of the results for accuracy and performance, it was identified that the consensus-based approach of ensuring the trustworthiness of crowdsourcing participants is highly affected by the crowd’s biases and the Hawthorne effect. Therefore, a comparison and analysis of the annotation quality of the crowdsourcing platform with consensus, reputation, and gold v standard-based approaches were conducted and a model to measure the trustworthiness of crowd response was developed. The major outcome of this research is the crowdsourcing platform that can be used for local annotation processes with the assurance of worker reliability. The number of tasks completed by the workers within a given period, the number of tasks attempted by each worker within a given period, the percentage of tasks completed compared to tasks attempted, time taken to complete tasks, the accuracy of responses considering golden rules, time taken to submit responses after each task assignment and the consistency of response time provided were identified as the quantitative measurements to assess the trustworthiness of workers. After this identification, the relationship between reputation score, performance score and bias score was formulated by analysing the worker responses. The worker behaviour model and trust measurement model showed an accuracy of 87% and 91% respectively after comparing with the expert response score which can be further improved by incorporating contextual analysis, worker belief and opinion analysis. The proposed methodology would accelerate data collection, enhance data quality, and would promote the development of high-quality labelled datasets. Keywords: Annotation, Collaboration, Crowdsourcing, Human-Computer Interaction, Trustworthiness

Browse

Browsing Doctor of Philosophy (Ph.D.) by Subject "COMPUTER SCIENCE - Dissertation"