Michael M. Tadesse

Bio: Michael M. Tadesse is an academic researcher from Dalian University of Technology. The author has contributed to research in topics: Sentence & Social media. The author has an hindex of 4, co-authored 6 publications receiving 145 citations.

Detection of Depression-Related Posts in Reddit Social Media Forum

Abstract: Depression is viewed as the largest contributor to global disability and a major reason for suicide. It has an impact on the language usage reflected in the written text. The key objective of our study is to examine Reddit users' posts to detect any factors that may reveal the depression attitudes of relevant online users. For such purpose, we employ the Natural Language Processing (NLP) techniques and machine learning approaches to train the data and evaluate the efficiency of our proposed method. We identify a lexicon of terms that are more common among depressed accounts. The results show that our proposed method can significantly improve performance accuracy. The best single feature is bigram with the Support Vector Machine (SVM) classifier to detect depression with 80% accuracy and 0.80 F1 scores. The strength and effectiveness of the combined features (LIWC+LDA+bigram) are most successfully demonstrated with the Multilayer Perceptron (MLP) classifier resulting in the top performance for depression detection reaching 91% accuracy and 0.93 F1 scores. According to our study, better performance improvement can be achieved by proper feature selections and their multiple feature combinations.

Personality Predictions Based on User Behavior on the Facebook Social Media Platform

Abstract: With the development of social networks, a large variety of approaches have been developed to define users’ personalities based on their social activities and language use habits. Particular approaches differ with regard to different machine learning algorithms, data sources, and feature sets. The goal of this paper is to investigate the predictability of the personality traits of Facebook users based on different features and measures of the Big 5 model. We examine the presence of structures of social networks and linguistic features relative to personality interactions using the myPersonality project data set. We analyze and compare four machine learning models and perform the correlation between each of the feature sets and personality traits. The results for the prediction accuracy show that even if tested under the same data set, the personality prediction system built on the XGBoost classifier outperforms the average baseline for all the feature sets, with a highest prediction accuracy of 74.2%. The best prediction performance was reached for the extraversion trait by using the individual social network analysis features set, which achieved a higher personality prediction accuracy of 78.6%.

Detection of Suicide Ideation in Social Media Forums Using Deep Learning

Abstract: Suicide ideation expressed in social media has an impact on language usage. Many at-risk individuals use social forum platforms to discuss their problems or get access to information on similar tasks. The key objective of our study is to present ongoing work on automatic recognition of suicidal posts. We address the early detection of suicide ideation through deep learning and machine learning-based classification approaches applied to Reddit social media. For such purpose, we employ an LSTM-CNN combined model to evaluate and compare to other classification models. Our experiment shows the combined neural network architecture with word embedding techniques can achieve the best relevance classification results. Additionally, our results support the strength and ability of deep learning architectures to build an effective model for a suicide risk assessment in various text classification tasks.

Interactive Self-Attentive Siamese Network for Biomedical Sentence Similarity

Abstract: The determination of semantic similarity between sentences is an important component in natural language processing (NLP) tasks such as text retrieval and text summarization. Many approaches have been proposed for estimating sentence similarity, and Siamese neural networks (SNN) provide a better approach. However, the sentence semantic representation, generated by sharing weights in the SNN without any attention mechanism, ignores the different contributions of different words to the overall sentence semantics. Furthermore, the attention operation within only a single sentence neglects interactive semantic influence on similarity estimation. To address these issues, an interactive self-attention (ISA) mechanism is proposed in this paper and integrated with an SNN, named an interactive self-attentive Siamese neural network (ISA-SNN) which is used to verify the effectiveness of ISA. The proposed model obtains the weights of words in a single sentence by means of self-attention and extracts inherent interactive semantic information between sentences via interactive attention to enhance sentence semantic representation. It achieves better performances without feature engineering than other existing methods on three biomedical benchmark datasets (a Pearson correlation coefficient of 0.656 and 0.713/0.658 on DBMI and CDD-ful/-ref, respectively).

Chinese medical relation extraction based on multi-hop self-attention mechanism

Abstract: The medical literature is the most important way to demonstrate academic achievements and academic exchanges. Massive medical literature has become a huge treasure trove of knowledge. It is necessary to automatically extract implicit medical knowledge from the medical literature. Medical relation extraction aims to automatically extract medical relations from the medical text for various medical researches. However, there are a few kinds of research in Chinese medical literature. Currently, the popular methods are based on neural networks, which focus on semantic information on one aspect of the sentence. However, complex semantic information in the sentence determines the relation between entities, the semantic information cannot be represented by one sentence vector. In this paper, we propose an attention-based model to extract the multi-aspect semantic information for the Chinese medical relation extraction by multi-hop attention mechanism. The model could generate multiple weight vectors for the sentence through each attention step, therefore, we can generate the different semantic representation of a sentence, respectively. Our model is evaluated by using Chinese medical literature from China National Knowledge Infrastructure (CNKI). It achieves an F1 score of 93.19% for therapeutic relation tasks and 73.47% for causal relation tasks.

Suicidal Ideation Detection: A Review of Machine Learning Methods and Applications

Abstract: Suicide is a critical issue in modern society. Early detection and prevention of suicide attempts should be addressed to save people's life. Current suicidal ideation detection methods include clinical methods based on the interaction between social workers or experts and the targeted individuals and machine learning techniques with feature engineering or deep learning for automatic detection based on online social contents. This paper is the first survey that comprehensively introduces and discusses the methods from these categories. Domain-specific applications of suicidal ideation detection are reviewed according to their data sources, i.e., questionnaires, electronic health records, suicide notes, and online user content. Several specific tasks and datasets are introduced and summarized to facilitate further research. Finally, we summarize the limitations of current work and provide an outlook of further research directions.

CHI '11 Extended Abstracts on Human Factors in Computing Systems

Abstract: Over the last year or so, we have been blessed with the challenge, the opportunity, and the distinct pleasure of organizing the CHI 2011 Conference on Human Factors in Computing Systems, the premier international conference for the field of human-computer interaction. CHI 2011 takes place in gorgeous, energetic, sophisticated Vancouver BC, a city renowned for its innovation in entertainment, sustainability, accessibility, and inclusivity. The New York Times calls it, "a liquid city, a tomorrow city, equal parts India, China, England, France and the Pacific Northwest." Vancouver lays a beautiful backdrop for our conference, which boasts nearly 30 years of wonderful work. Behind the success of the conference is our diverse community of faculty and students, of researchers and practitioners, of young and, well, also of experienced. It is a community of designers, technologists, psychologists, social scientists, biologists, artists, engineers, anthropologists, musicians; the list goes on. Wherever we are, we are always a community of near and far. Most impressively, ours is a community that cares deeply about innovating, learning, sharing, and interacting; all with the common goal of using technology to shape the way people around the world live and play. Returning attendees will recognize the general conference format - 2 days of small intimate workshops, followed by 4 days of technical content, all surrounded by social and intellectual exchanges. In addition to the familiar venues that form the core of the conference, we have also arranged various special events, such an keynotes by Howard Rheingold and Ethan Zuckerman; invited talks by ACM SIGCHI award winners Terry Winograd, Larry Tesler, Alan Newell, and Clayton Lewis; an HCI museum exhibit hosted by Bill Buxton; and a panel celebrating Stu Card's achievements and contributions to the field of HCI. With the record number of submissions and accepted content this year, we hope that you will utilize the print and electronic programs, but also the daily CHI Madness presentations that provide a glimpse of the day ahead. In the interest of continuing to evolve the conference to best serve our needs, we will experiment with shorter talks this year (20 minute slots for long pieces of content and 10 for shorter ones) to infuse even more energy into the program. We will also have a pretty full slate of social media applications to help you connect with other attendees and to provide you with the fullest experience possible.

AI in mental health.

Abstract: With the advent of digital approaches to mental health, modern artificial intelligence (AI), and machine learning in particular, is being used in the development of prediction, detection and treatment solutions for mental health care. In terms of treatment, AI is being incorporated into digital interventions, particularly web and smartphone apps, to enhance user experience and optimise personalised mental health care. In terms of prediction and detection, modern streams of abundant data mean that data-driven AI methods can be employed to develop prediction/detection models for mental health conditions. In particular, an individual’s ‘digital exhaust’, the data gathered from their numerous personal digital device and social media interactions, can be mined for behavioural or mental health insights. Language, long considered a window into the human mind, can now be quantitatively harnessed as data with powerful computer-based natural language processing to also provide a method of inferring mental health. Furthermore, natural language processing can also be used to develop conversational agents used for therapeutic intervention.

User OCEAN Personality Model Construction Method Using a BP Neural Network

Abstract: In the era of big data, the Internet is enmeshed in people’s lives and brings conveniences to their production and lives. The analysis of user preferences and behavioral predictions of user data can provide references for optimizing information structure and improving service accuracy. According to the present research, user’s behavior on social networking sites has a great correlation with their personality, and the five characteristics of the OCEAN (Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism) personality model can cover all aspects of a user’s personality. It is important in identifying a user’s OCEAN personality model to analyze their digital footprints left on social networking sites and to extract the rules of users’ behavior, and then to make predictions about user behavior. In this paper, the Latent Dirichlet Allocation (LDA) topic model is first used to extract the user’s text features. Second, the extracted features are used as sample input for a BP neural network. The results of the user’s OCEAN personality model obtained by a questionnaire are used as sample output for a BP neural network. Finally, the neural network is trained. A mapping model between the probability of the user’s text topic and their OCEAN personality model is established to predict the latter. The results show that the present approach improves the efficiency and accuracy of such a prediction.

An Emotional Recommender System for Music

Abstract: Nowadays, recommender systems have become essential to users for finding “what they need” within large collections of items. Meanwhile, recent studies have demonstrated as user personality can effectively provide a more valuable information to significantly improve recommenders’ performance, especially considering behavioral data captured from social network logs. In this work, we describe a novel music recommendation technique based on the identification of personality traits, moods, and emotions of a single user, starting from solid psychological observations recognized by the analysis of user behavior within a social environment. In particular, users’ personality and mood have been embedded within a content-based filtering approach to obtain more accurate and dynamic results. Several experiments are then reported to show effectiveness of user personality and mood recognition recommendation, thus, encouraging research in this direction.