Research Publications Authored by SLIIT Staff
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This collection includes all SLIIT staff publications presented at external conferences and published in external journals. The materials are organized by faculty to facilitate easy retrieval.
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Publication Embargo Characterisation of Mental Health Conditions in Social Media Using Deep Learning Techniques(Springer, Cham, 2022-09-19) Sharma, T; Panchendrarajan, R; Saxena, ASocial media has become the easiest and most popular choice for online users to share their views, thoughts, opinions, and emotions with their friends and followers. The shared content is very helpful in making valuable conclusions about an individual’s personality. Nowadays, researchers across the world are collecting social media data for understanding the mental health of social media users. Mental illness is a big concern in today’s society, and in no time, it can turn into suicidal thoughts if efficacious methods are not taken into account. Early detection of such mental health conditions provides a potential way for effective social intervention. This has opened up opportunities to the research community to automatically determine various mental health conditions, such as anxiety, depression, and near-suicidal thoughts from user-generated content. The deep learning techniques have been extensively used in this research area to better understand the mental health of users on social networking platforms. In this chapter, we conduct a comprehensive review of the research works that use deep learning techniques to identify various mental health conditions from social media data. We discuss methods to detect different types of mental health conditions, such as anxiety, depression, stress, suicide, and anorexia. We also provide the details of the datasets used in these studies. The chapter is concluded with promising future directions.Publication Embargo Know More: Social Media based Student Centric E-learning platform with Machine Learning Approaches(IEEE, 2022-07-18) Malavige, O; Nasome, V; Costa, M; Jayasinghe, B; Karunasena, A; Samarakoon, USocial media has become increasingly popular among the younger generation in the last decade. Students engage with social media on daily basis, and it affects their interests, lifestyle, and attitude. There are many existing e-learning applications used by higher educational institutes, but such applications are mainly focused on delivering teaching content rather than facilitating active and interactive learning. This paper proposes a novel e-learning platform to create an active and interactive learning environment for students leveraging social media strategies, especially those of “Facebook.” The objective of this platform is to promote self-motivation, self-learning, and interaction. The platform features were built on considering three aspects important for learning, which are personal knowledge management, learning management, and collaborative learning. Features of the proposed platform that it comprises are Newsfeed, Classmates, Profile, Cluster, Repository, Knowledgebase, Bookmark, Topic Map, Search Engine, Test Mark Prediction, and Slide Show Summary generator. Machine Learning techniques and Natural Language Processing were used to build some of the platform features. The feedback collected on the proposed system, “KnowMore,” shows that the satisfaction of the students has increased with the system.Publication Open Access A Singlish Supported Post Recommendation Approach for Social Media(SCITEPRESS – Science and Technology Publications, 2022-01) Sandamini, U; Rathnakumara, K; Pramuditha, p; Dissanayake, M; Sriyaratna, D; De Silva, H; Kasthurirathna, DSocial media is an attractive means of communication which people used to exchange information. Post recommendation eliminates the overflooding of information in social media to the users’ news feed by suggesting the best matching information based on users’ preference that in return increase the usability. Social media users use different languages and their variations where most of the Sri Lankan users are accustomed to use Sinhala and Romanized Sinhala. However, post recommendation approaches used in current social media applications do not cater to code-mixed text. Therefore, this paper proposes a novel post recommendation approach that supports Singlish. The study is separated into two major components as language identification and transliteration, and post recommendation. In this study, script identification was performed using regular expressions while a Naïve Bayes classification model that accomplished 97% of accuracy was employed for language identification of Romanized text. Transliteration of Singlish to Sinhala was conducted using a character level seq2seq BLSTM model with a BLEU score of 0.94. Furthermore, Google translation API and YAKE were used for Sinhala-English translation and keyword extraction respectively. Post recommendation model utilized a combination of rule-based and CF techniques that accomplished the RMSE of 0.2971 and MAE of 0.2304.Publication Open Access Effectiveness of Celebrity Endorsement on Social Media towards Consumer’s Purchase Intention(researchgate.net, 2017-02) Samarasinghe, U. SIn spite of numerous theoretical and empirical studies that examine celebrity endorsement on traditional media, few studies have attempted to examine the relationship between Source Characteristics and Consumer Purchase Intention in the domain of social media. Furthermore whether this relationship is differ from Male to Female is not empirically investigated. This study combines Source credibility and Source Attractiveness theories to oversee Source Characteristics factors on Consumer’s Purchasing Intention. As a result Source Attractiveness dimensions namely Source Familiarity, Source Likeability and Source Similarity have been introduced to the Source Characteristics which is the main theoretical contribution. A sample of 338 consumers who use social media for FMCG product purchase decisions responded to the survey. The results provided preliminary support for the hypothesized model. It was found that there is a positive relationship between Source Expertness, Source Trustworthiness, Source Likeability, Source Similarity and Consumer’s Purchase Intention while there is a negative relationship between Source Familiarity and Consumer’s Purchase Intention. The study further revealed that the impact of Source Expertness, Source Trustworthiness, Source Likeability, and Source Similarity is different for male and female consumers. Thus, in promotional campaigns a product which is used by only one particular gender has to concern itself with the characteristics of that particular gender, and Source Familiarity should not concern itself with gender diversity in celebrity endorsement since it has no impact on the relationship between Source Familiarity and a Consumer’s Purchase Intention.Publication Open Access Social Media and Celebrity Endorsement: An analysis of Literature Review(researchgate.net, 2019-01) Samarasinghe, U. CThe purpose of this chapter is to provide a thorough review in existent literature in relation to the social media and celebrity endorsement. In view of that, this paper examines key concepts pertaining to social media, celebrity endorsement, Studies based on Source Characteristics, which are Source Expertise, Source Trustworthiness, Source Familiarity, Source Similarity and Source Likability .Each concept discussed in light of what past literature has stated in terms of the definitions, conceptualization, key arguments, antecedents and consequences, to offer a complete depiction of the theme.Publication Embargo Sentiment Classification of Sinhala Content in Social Media: An Ensemble Approach(IEEE, 2021-12-09) Jayasuriya, P; Munasinghe, R; Thelijjagoda, SWe focus on the binary classification of Sinhala social media content in the sports domain using machine learning algorithms. In particular, we improve upon the accuracy achieved in a previous study of ours that utilized word and character N-grams. We use the base learners from that study to implement a probability-based stacking ensemble approach. This is done by creating a base learner library of 1066 base learners, using 13 different algorithms and different N-gram feature extraction methods. Different base learner combinations from the library are then stacked together to find the best stacking ensemble model. The best stacking ensemble model achieves an accuracy of 83.8% which is an improvement of over 1.5% of our previous study.Publication Embargo Sentiment Classification of Sinhala Content in Social Media: A Comparison between Stemmers and N-gram Features(IEEE, 2021-12-09) Jayasuriya, P; Munasinghe, R; Thelijjagoda, SSentiment classification for non-English languages has gained significant attention from researchers in the past few years with the increasing use of non-English scripts and Romanized scripts for expressing sentiments over social media. In this study, we begin by classifying Sinhala sentiments on social media into positive and negative polarity classes using N-gram feature extraction. N-grams are a contiguous sequence of words or characters of a text. Then we focus on improving the classification accuracy by employing different stemming methods. Stemming is generally used to reduce the dimensionality of the feature set - something which needs to be carried out with great care as over reducing feature dimensionality causes the classification accuracy to decrease. Finally, we compare the accuracy and efficiency of N-gram feature extraction and stemming based sentiment analysis models.Publication Open Access Social media adoption: Small and medium-sized enterprises' perspective in Sri Lanka(Korea Distribution Science Association, 2021) SAMSUDEEN, S. N; THELIJJAGODA, S; SANJEETHA, M. B. FIn this digital age, all organizational environments force businesses to adopt Information and Communication Technologies (ICT) since these technologies have immense impact on such businesses' competitiveness and productivity. Nonetheless, the productivity and the competitiveness enjoyed by such firms vary depending on the size or the organizations, context of the country; developing or developed, and what kinds of technologies are adopted. This investigation focused on small- and medium-sized enterprises (SMEs) of Eastern province of Sri Lanka where such studies are scanty. The adoption of social media (SM) by SMEs is inclined to change how organizations operate, this calls for an investigation of the elements that impact SMEs to adopt SM and such investigation. Technology-Organization-Environment (TOE) framework was based to understand the factors. Research approach was quantitative approach using questionnaire survey. Data were collected using online form to see 285 valid responses. Structural Equation Modelling was deployed to evaluate the proposed model. Results revealed that Relative Advantage, Compatibility, Complexity, Observability, Competitive Intensity, Bandwagon Pressure, and Competitive Pressure were influencing, while Trialability, Top Management Support, CEO's Innovativeness did not show statistically significant influence on SMEs' social media adoption.