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  1. AU="Cristea, Alexandra I"
  2. AU="Sakr, Hader I"
  3. AU="Shikora, Scott A."
  4. AU="Raza Naqvi"
  5. AU="Chin Fatt, Cherise R"
  6. AU="Leena Sahlström"
  7. AU="Karkare, Siddharth"
  8. AU="Corrêa, Diogo G"
  9. AU="Choi, Sun Ha"

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  1. Artikel: Editorial: New challenges and future perspectives in cognitive neuroscience.

    Frantzidis, Christos A / Peristeri, Eleni / Andreou, Maria / Cristea, Alexandra I

    Frontiers in human neuroscience

    2024  Band 18, Seite(n) 1390788

    Sprache Englisch
    Erscheinungsdatum 2024-03-08
    Erscheinungsland Switzerland
    Dokumenttyp Editorial
    ZDB-ID 2425477-0
    ISSN 1662-5161
    ISSN 1662-5161
    DOI 10.3389/fnhum.2024.1390788
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  2. Artikel ; Online: Learners Demographics Classification on MOOCs During the COVID-19: Author Profiling via Deep Learning Based on Semantic and Syntactic Representations.

    Aljohani, Tahani / Cristea, Alexandra I

    Frontiers in research metrics and analytics

    2021  Band 6, Seite(n) 673928

    Abstract: Massive Open Online Courses (MOOCs) have become universal learning resources, and the COVID-19 pandemic is rendering these platforms even more necessary. In this paper, we seek to improve Learner Profiling (LP), i.e. estimating the demographic ... ...

    Abstract Massive Open Online Courses (MOOCs) have become universal learning resources, and the COVID-19 pandemic is rendering these platforms even more necessary. In this paper, we seek to improve Learner Profiling (LP), i.e. estimating the demographic characteristics of learners in MOOC platforms. We have focused on examining models which show promise elsewhere, but were never examined in the LP area (deep learning models) based on effective textual representations. As LP characteristics, we predict here the
    Sprache Englisch
    Erscheinungsdatum 2021-08-02
    Erscheinungsland Switzerland
    Dokumenttyp Journal Article
    ISSN 2504-0537
    ISSN (online) 2504-0537
    DOI 10.3389/frma.2021.673928
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  3. Buch ; Online: Effect of emotions and personalisation on cancer website reuse intentions

    Hadzidedic, Suncica / Cristea, Alexandra I. / Watson, Derrick G.

    2023  

    Abstract: The effect of emotions and personalisation on continuance use intentions in online health services is underexplored. Accordingly, we propose a research model for examining the impact of emotion- and personalisation-based factors on cancer website reuse ... ...

    Abstract The effect of emotions and personalisation on continuance use intentions in online health services is underexplored. Accordingly, we propose a research model for examining the impact of emotion- and personalisation-based factors on cancer website reuse intentions. We conducted a study using a real-world NGO cancer-support website, which was evaluated by 98 participants via an online questionnaire. Model relations were estimated using the PLS-SEM method. Our findings indicated that pre-use emotions did not significantly influence perceived personalisation. However, satisfaction with personalisation, and perceived usefulness mediated by satisfaction, increased reuse intentions. In addition, post-use positive emotions potentially influenced reuse intentions. Our paper, therefore, illustrates the applicability of theory regarding continuance use intentions to cancer-support websites and highlights the importance of personalisation for these purposes.

    Comment: 19 pages, 4 figures, 3 tables
    Schlagwörter Computer Science - Human-Computer Interaction
    Thema/Rubrik (Code) 150
    Erscheinungsdatum 2023-01-02
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    Dokumenttyp Buch ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  4. Artikel ; Online: Detecting Fine-Grained Emotions on Social Media during Major Disease Outbreaks: Health and Well-being before and during the COVID-19 Pandemic.

    Aduragba, Olanrewaju Tahir / Yu, Jialin / Cristea, Alexandra I / Shi, Lei

    AMIA ... Annual Symposium proceedings. AMIA Symposium

    2022  Band 2021, Seite(n) 187–196

    Abstract: The COVID-19 pandemic has affected the whole world in various ways. One type of impact is that communication, work, interaction, a great part of our lives has moved online on various platforms, with some of the most popular being the social media ones. ... ...

    Abstract The COVID-19 pandemic has affected the whole world in various ways. One type of impact is that communication, work, interaction, a great part of our lives has moved online on various platforms, with some of the most popular being the social media ones. Another, arguably less visible impact, is the emotional impact. Detecting and understanding emotions is important, to better discern the emotional health and well-being of the global population. Thus, in this work, we use a social media platform (Twitter) to analyse emotions in detail. Our contribution is twofold: (1) we propose EmoBERT, a new emotion-based variant of the BERT transformer model, able to learn emotion representations and outperform the state-of-the-art; (2) we provide a fine-grained analysis of the pandemic's effect in a major location, London, comparing specific emotions (annoyed, anxious, empathetic, sad) before and during the epidemic.
    Mesh-Begriff(e) COVID-19/epidemiology ; Disease Outbreaks ; Emotions ; Humans ; Pandemics ; Social Media
    Sprache Englisch
    Erscheinungsdatum 2022-02-21
    Erscheinungsland United States
    Dokumenttyp Journal Article
    ISSN 1942-597X
    ISSN (online) 1942-597X
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  5. Artikel: Using deep learning to analyze the psychological effects of COVID-19.

    Almeqren, Monira Abdulrahman / Almuqren, Latifah / Alhayan, Fatimah / Cristea, Alexandra I / Pennington, Diane

    Frontiers in psychology

    2023  Band 14, Seite(n) 962854

    Abstract: Problem: Sentiment Analysis (SA) automates the classification of the sentiment of people's attitudes, feelings or reviews employing natural language processing (NLP) and computational approaches. Deep learning has recently demonstrated remarkable ... ...

    Abstract Problem: Sentiment Analysis (SA) automates the classification of the sentiment of people's attitudes, feelings or reviews employing natural language processing (NLP) and computational approaches. Deep learning has recently demonstrated remarkable success in the field of SA in many languages including Arabic. Arabic sentiment analysis, however, still has to be improved, due to the complexity of the Arabic language's structure, the variety of dialects, and the lack of lexicons. Moreover, in Arabic, anxiety as a psychological sentiment has not been the target of much research.
    Aim: This paper aims to provide solutions to one of the challenges of Arabic Sentiment Analysis (ASA) using a deep learning model focused on predicting the anxiety level during COVID-19 in Saudi Arabia.
    Methods: A psychological scale to determine the level of anxiety was built and validated. It was then used to create the Arabic Psychological Lexicon (AraPh) containing 138 different dialectical Arabic words that express anxiety, which was used to annotate our corpus (Aranxiety). Aranxiety comprises 955 Arabic tweets representing the level of user anxiety during COVID-19. Bi-GRU model with word embedding was then applied to analyze the sentiment of the tweets and to determine the anxiety level.
    Results: For SA, the applied model achieved 88% on accuracy, 89% on precision, 88% on recall, and 87% for F1. A majority of 77% of tweets presented no anxiety, whereas 17% represented mild anxiety and a mere 6% represented high anxiety.
    Conclusion: The proposed model can be used by the Saudi Ministry of Health and members of the research community to formulate solutions to increase psychological resiliency among the Saudi population.
    Sprache Englisch
    Erscheinungsdatum 2023-08-14
    Erscheinungsland Switzerland
    Dokumenttyp Journal Article
    ZDB-ID 2563826-9
    ISSN 1664-1078
    ISSN 1664-1078
    DOI 10.3389/fpsyg.2023.962854
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  6. Buch ; Online: Incorporating Emotions into Health Mention Classification Task on Social Media

    Aduragba, Olanrewaju Tahir / Yu, Jialin / Cristea, Alexandra I.

    2022  

    Abstract: The health mention classification (HMC) task is the process of identifying and classifying mentions of health-related concepts in text. This can be useful for identifying and tracking the spread of diseases through social media posts. However, this is a ... ...

    Abstract The health mention classification (HMC) task is the process of identifying and classifying mentions of health-related concepts in text. This can be useful for identifying and tracking the spread of diseases through social media posts. However, this is a non-trivial task. Here we build on recent studies suggesting that using emotional information may improve upon this task. Our study results in a framework for health mention classification that incorporates affective features. We present two methods, an intermediate task fine-tuning approach (implicit) and a multi-feature fusion approach (explicit) to incorporate emotions into our target task of HMC. We evaluated our approach on 5 HMC-related datasets from different social media platforms including three from Twitter, one from Reddit and another from a combination of social media sources. Extensive experiments demonstrate that our approach results in statistically significant performance gains on HMC tasks. By using the multi-feature fusion approach, we achieve at least a 3% improvement in F1 score over BERT baselines across all datasets. We also show that considering only negative emotions does not significantly affect performance on the HMC task. Additionally, our results indicate that HMC models infused with emotional knowledge are an effective alternative, especially when other HMC datasets are unavailable for domain-specific fine-tuning. The source code for our models is freely available at https://github.com/tahirlanre/Emotion_PHM.
    Schlagwörter Computer Science - Computation and Language ; Computer Science - Machine Learning
    Thema/Rubrik (Code) 006
    Erscheinungsdatum 2022-12-09
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    Dokumenttyp Buch ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  7. Buch ; Online: Multi-task Learning for Personal Health Mention Detection on Social Media

    Aduragba, Olanrewaju Tahir / Yu, Jialin / Cristea, Alexandra I.

    2022  

    Abstract: Detecting personal health mentions on social media is essential to complement existing health surveillance systems. However, annotating data for detecting health mentions at a large scale is a challenging task. This research employs a multitask learning ... ...

    Abstract Detecting personal health mentions on social media is essential to complement existing health surveillance systems. However, annotating data for detecting health mentions at a large scale is a challenging task. This research employs a multitask learning framework to leverage available annotated data from a related task to improve the performance on the main task to detect personal health experiences mentioned in social media texts. Specifically, we focus on incorporating emotional information into our target task by using emotion detection as an auxiliary task. Our approach significantly improves a wide range of personal health mention detection tasks compared to a strong state-of-the-art baseline.

    Comment: 5 pages
    Schlagwörter Computer Science - Computation and Language ; Computer Science - Artificial Intelligence
    Erscheinungsdatum 2022-12-09
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    Dokumenttyp Buch ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  8. Artikel ; Online: Editorial: Artificial intelligence techniques for personalized educational software.

    Troussas, Christos / Krouska, Akrivi / Kabassi, Katerina / Sgouropoulou, Cleo / Cristea, Alexandra I

    Frontiers in artificial intelligence

    2022  Band 5, Seite(n) 988289

    Sprache Englisch
    Erscheinungsdatum 2022-08-10
    Erscheinungsland Switzerland
    Dokumenttyp Editorial
    ISSN 2624-8212
    ISSN (online) 2624-8212
    DOI 10.3389/frai.2022.988289
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  9. Buch ; Online: Religion and Spirituality on Social Media in the Aftermath of the Global Pandemic

    Aduragba, Olanrewaju Tahir / Cristea, Alexandra I. / Phillips, Pete / Kurlberg, Jonas / Yu, Jialin

    2022  

    Abstract: During the COVID-19 pandemic, the Church closed its physical doors for the first time in about 800 years, which is, arguably, a cataclysmic event. Other religions have found themselves in a similar situation, and they were practically forced to move ... ...

    Abstract During the COVID-19 pandemic, the Church closed its physical doors for the first time in about 800 years, which is, arguably, a cataclysmic event. Other religions have found themselves in a similar situation, and they were practically forced to move online, which is an unprecedented occasion. In this paper, we analyse this sudden change in religious activities twofold: we create and deliver a questionnaire, as well as analyse Twitter data, to understand people's perceptions and activities related to religious activities online. Importantly, we also analyse the temporal variations in this process by analysing a period of 3 months: July-September 2020. Additionally to the separate analysis of the two data sources, we also discuss the implications from triangulating the results.

    Comment: Code used for this paper is available at: https://github.com/tahirlanre/covid19-online-religion
    Schlagwörter Computer Science - Computers and Society ; Computer Science - Artificial Intelligence ; Computer Science - Computation and Language ; Computer Science - Social and Information Networks
    Erscheinungsdatum 2022-12-11
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  10. Buch ; Online: Language as a Latent Sequence

    Yu, Jialin / Cristea, Alexandra I. / Harit, Anoushka / Sun, Zhongtian / Aduragba, Olanrewaju Tahir / Shi, Lei / Moubayed, Noura Al

    deep latent variable models for semi-supervised paraphrase generation

    2023  

    Abstract: This paper explores deep latent variable models for semi-supervised paraphrase generation, where the missing target pair for unlabelled data is modelled as a latent paraphrase sequence. We present a novel unsupervised model named variational sequence ... ...

    Abstract This paper explores deep latent variable models for semi-supervised paraphrase generation, where the missing target pair for unlabelled data is modelled as a latent paraphrase sequence. We present a novel unsupervised model named variational sequence auto-encoding reconstruction (VSAR), which performs latent sequence inference given an observed text. To leverage information from text pairs, we additionally introduce a novel supervised model we call dual directional learning (DDL), which is designed to integrate with our proposed VSAR model. Combining VSAR with DDL (DDL+VSAR) enables us to conduct semi-supervised learning. Still, the combined model suffers from a cold-start problem. To further combat this issue, we propose an improved weight initialisation solution, leading to a novel two-stage training scheme we call knowledge-reinforced-learning (KRL). Our empirical evaluations suggest that the combined model yields competitive performance against the state-of-the-art supervised baselines on complete data. Furthermore, in scenarios where only a fraction of the labelled pairs are available, our combined model consistently outperforms the strong supervised model baseline (DDL) by a significant margin (p <.05; Wilcoxon test). Our code is publicly available at "https://github.com/jialin-yu/latent-sequence-paraphrase".
    Schlagwörter Computer Science - Computation and Language ; Computer Science - Machine Learning
    Thema/Rubrik (Code) 006 ; 004
    Erscheinungsdatum 2023-01-05
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    Dokumenttyp Buch ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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