scholarly journals Looking for Razors and Needles in a Haystack: Multifaceted Analysis of Suicidal Declarations on Social Media—A Pragmalinguistic Approach

2021 ◽  
Vol 18 (22) ◽  
pp. 11759
Author(s):  
Michal Ptaszynski ◽  
Monika Zasko-Zielinska ◽  
Michal Marcinczuk ◽  
Gniewosz Leliwa ◽  
Marcin Fortuna ◽  
...  
Keyword(s):  
Social Media ◽  
High Performance ◽  
Large Scale ◽  
Annotation Scheme ◽  
Contextual Processing ◽  
Wide Range ◽  
Before And After ◽  
Media Platform ◽  
Future Work ◽  
Multifaceted Analysis

In this paper, we study language used by suicidal users on Reddit social media platform. To do that, we firstly collect a large-scale dataset of Reddit posts and annotate it with highly trained and expert annotators under a rigorous annotation scheme. Next, we perform a multifaceted analysis of the dataset, including: (1) the analysis of user activity before and after posting a suicidal message, and (2) a pragmalinguistic study on the vocabulary used by suicidal users. In the second part of the analysis, we apply LIWC, a dictionary-based toolset widely used in psychology and linguistic research, which provides a wide range of linguistic category annotations on text. However, since raw LIWC scores are not sufficiently reliable, or informative, we propose a procedure to decrease the possibility of unreliable and misleading LIWC scores leading to misleading conclusions by analyzing not each category separately, but in pairs with other categories. The analysis of the results supported the validity of the proposed approach by revealing a number of valuable information on the vocabulary used by suicidal users and helped to pin-point false predictors. For example, we were able to specify that death-related words, typically associated with suicidal posts in the majority of the literature, become false predictors, when they co-occur with apostrophes, even in high-risk subreddits. On the other hand, the category-pair based disambiguation helped to specify that death becomes a predictor only when co-occurring with future-focused language, informal language, discrepancy, or 1st person pronouns. The promising applicability of the approach was additionally analyzed for its limitations, where we found out that although LIWC is a useful and easily applicable tool, the lack of any contextual processing makes it unsuitable for application in psychological and linguistic studies. We conclude that disadvantages of LIWC can be easily overcome by creating a number of high-performance AI-based classifiers trained for annotation of similar categories as LIWC, which we plan to pursue in future work.

RepPer: Perception of Psychiatric Disorders on Twitter in French (Preprint)

2020 ◽  
Author(s):  
Sarah Delanys ◽  
Farah Benamara ◽  
Véronique Moriceau ◽  
François Olivier ◽  
Josiane Mothe
Keyword(s):  
Social Media ◽  
Psychiatric Disorders ◽  
Digital Technology ◽  
Psychotic Disorders ◽  
Negative Polarity ◽  
Machine Learning Algorithms ◽  
Annotation Scheme ◽  
Word Use ◽  
Wide Range ◽  
Initial Dataset

BACKGROUND With the advent of digital technology and specifically user generated contents in social media, new ways emerged for studying possible stigma of people in relation with mental health. Several pieces of work studied the discourse conveyed about psychiatric pathologies on Twitter considering mostly tweets in English and a limited number of psychiatric disorders terms. This paper proposes the first study to analyze the use of a wide range of psychiatric terms in tweets in French. OBJECTIVE Our aim is to study how generic, nosographic and therapeutic psychiatric terms are used on Twitter in French. More specifically, our study has three complementary goals: (1) to analyze the types of psychiatric word use namely medical, misuse, irrelevant, (2) to analyze the polarity conveyed in the tweets that use these terms (positive/negative/neural), and (3) to compare the frequency of these terms to those observed in related work (mainly in English ). METHODS Our study has been conducted on a corpus of tweets in French posted between 01/01/2016 to 12/31/2018 and collected using dedicated keywords. The corpus has been manually annotated by clinical psychiatrists following a multilayer annotation scheme that includes the type of word use and the opinion orientation of the tweet. Two analysis have been performed. First a qualitative analysis to measure the reliability of the produced manual annotation, then a quantitative analysis considering mainly term frequency in each layer and exploring the interactions between them. RESULTS One of the first result is a resource as an annotated dataset . The initial dataset is composed of 22,579 tweets in French containing at least one of the selected psychiatric terms. From this set, experts in psychiatry randomly annotated 3,040 tweets that corresponds to the resource resulting from our work. The second result is the analysis of the annotations; it shows that terms are misused in 45.3% of the tweets and that their associated polarity is negative in 86.2% of the cases. When considering the three types of term use, 59.5% of the tweets are associated to a negative polarity. Misused terms related to psychotic disorders (55.5%) are more frequent to those related to mood disorders (26.5%). CONCLUSIONS Some psychiatric terms are misused in the corpora we studied; which is consistent with the results reported in related work in other languages. Thanks to the great diversity of studied terms, this work highlighted a disparity in the representations and ways of using psychiatric terms. Moreover, our study is important to help psychiatrists to be aware of the term use in new communication media such as social networks which are widely used. This study has the huge advantage to be reproducible thanks to the framework and guidelines we produced; so that the study could be renewed in order to analyze the evolution of term usage. While the newly build dataset is a valuable resource for other analytical studies, it could also serve to train machine learning algorithms to automatically identify stigma in social media.

scholarly journals Process Design Kit and Design Automation for Flexible Hybrid Electronics

2019 ◽  
Vol 16 (3) ◽  
pp. 117-123
Author(s):  
Tsung-Ching Huang ◽  
Ting Lei ◽  
Leilai Shao ◽  
Sridhar Sivapurapu ◽  
Madhavan Swaminathan ◽  
...  
Keyword(s):  
Process Design ◽  
Design Automation ◽  
High Performance ◽  
Large Scale ◽  
Low Cost ◽  
Thin Film Transistor ◽  
Solution Process ◽  
Oxide Semiconductor ◽  
Wearable Electronics ◽  
Wide Range

Abstract High-performance low-cost flexible hybrid electronics (FHE) are desirable for applications such as internet of things and wearable electronics. Carbon nanotube (CNT) thin-film transistor (TFT) is a promising candidate for high-performance FHE because of its high carrier mobility, superior mechanical flexibility, and material compatibility with low-cost printing and solution processes. Flexible sensors and peripheral CNT-TFT circuits, such as decoders, drivers, and sense amplifiers, can be printed and hybrid-integrated with thinned (<50 μm) silicon chips on soft, thin, and flexible substrates for a wide range of applications, from flexible displays to wearable medical devices. Here, we report (1) a process design kit (PDK) to enable FHE design automation for large-scale FHE circuits and (2) solution process-proven intellectual property blocks for TFT circuits design, including Pseudo-Complementary Metal-Oxide-Semiconductor (Pseudo-CMOS) flexible digital logic and analog amplifiers. The FHE-PDK is fully compatible with popular silicon design tools for design and simulation of hybrid-integrated flexible circuits.

What Makes Politicians’ Instagram Posts Popular? Analyzing Social Media Strategies of Candidates and Office Holders with Computer Vision

2020 ◽  
Vol 26 (1) ◽  
pp. 143-166
Author(s):  
Yilang Peng
Keyword(s):  
Computer Vision ◽  
Social Media ◽  
Political Communication ◽  
Large Scale ◽  
Visual Media ◽  
Audience Engagement ◽  
Self Presentation ◽  
Media Platform ◽  
Unsupervised Approach ◽  
Textual Content

Previous research on the success of politicians’ messages on social media has so far focused on a limited number of platforms, especially Facebook and Twitter, and predominately studied the effects of textual content. This research reported here applies computer vision analysis to a total of 59,020 image posts published by 172 Instagram accounts of U.S. politicians, both candidates and office holders, and examines how visual attributes influence audience engagement such as likes and comments. In particular, this study introduces an unsupervised approach that combines transfer learning and clustering techniques to discover hidden categories from large-scale visual data. The results reveal that different self-personalization strategies in visual media, for example, images featuring politicians in private, nonpolitical settings, showing faces, and displaying emotions, generally increase audience engagement. Yet, a significant portion of politician’s Instagram posts still fell into the traditional, “politics-as-usual” type of political communication, showing professional settings and activities. The analysis explains how self-personalization is embodied in specific visual portrayals and how different self-presentation strategies affect audience engagement on a popular but less studied social media platform.

scholarly journals Performance Evaluation of Modularity Based Community Detection Algorithms in Large Scale Networks

2014 ◽  
Vol 2014 ◽  
pp. 1-15 ◽  
Author(s):  
Vinícius da Fonseca Vieira ◽  
Carolina Ribeiro Xavier ◽  
Nelson Francisco Favilla Ebecken ◽  
Alexandre Gonçalves Evsukoff
Keyword(s):  
Spectral Method ◽  
Community Detection ◽  
High Performance ◽  
Large Scale ◽  
Fine Tuning ◽  
Detection Algorithms ◽  
Research Areas ◽  
Structure Detection ◽  
Wide Range ◽  
Large Scale Networks

Community structure detection is one of the major research areas of network science and it is particularly useful for large real networks applications. This work presents a deep study of the most discussed algorithms for community detection based on modularity measure: Newman’s spectral method using a fine-tuning stage and the method of Clauset, Newman, and Moore (CNM) with its variants. The computational complexity of the algorithms is analysed for the development of a high performance code to accelerate the execution of these algorithms without compromising the quality of the results, according to the modularity measure. The implemented code allows the generation of partitions with modularity values consistent with the literature and it overcomes 1 million nodes with Newman’s spectral method. The code was applied to a wide range of real networks and the performances of the algorithms are evaluated.

scholarly journals High-Sensitivity Flexible Pressure Sensor-Based 3D CNTs Sponge for Human–Computer Interaction

Polymers ◽  
2021 ◽  
Vol 13 (20) ◽  
pp. 3465
Author(s):  
Jianli Cui ◽  
Xueli Nan ◽  
Guirong Shao ◽  
Huixia Sun
Keyword(s):  
Pressure Sensor ◽  
High Performance ◽  
Large Scale ◽  
Low Cost ◽  
Pressure Sensors ◽  
Chemical Vapor ◽  
High Sensitivity ◽  
Wearable Electronics ◽  
Wide Range ◽  
Flexible Pressure Sensors

Researchers are showing an increasing interest in high-performance flexible pressure sensors owing to their potential uses in wearable electronics, bionic skin, and human–machine interactions, etc. However, the vast majority of these flexible pressure sensors require extensive nano-architectural design, which both complicates their manufacturing and is time-consuming. Thus, a low-cost technology which can be applied on a large scale is highly desirable for the manufacture of flexible pressure-sensitive materials that have a high sensitivity over a wide range of pressures. This work is based on the use of a three-dimensional elastic porous carbon nanotubes (CNTs) sponge as the conductive layer to fabricate a novel flexible piezoresistive sensor. The synthesis of a CNTs sponge was achieved by chemical vapor deposition, the basic underlying principle governing the sensing behavior of the CNTs sponge-based pressure sensor and was illustrated by employing in situ scanning electron microscopy. The CNTs sponge-based sensor has a quick response time of ~105 ms, a high sensitivity extending across a broad pressure range (less than 10 kPa for 809 kPa−1) and possesses an outstanding permanence over 4,000 cycles. Furthermore, a 16-pixel wireless sensor system was designed and a series of applications have been demonstrated. Its potential applications in the visualizing pressure distribution and an example of human–machine communication were also demonstrated.

scholarly journals Social Media-based User Embedding: A Literature Review

2019 ◽  
Author(s):  
Shimei Pan ◽  
Tao Ding
Keyword(s):  
Social Media ◽  
High Performance ◽  
Large Scale ◽  
Ground Truth ◽  
Representation Learning ◽  
Success Stories ◽  
Recent Success ◽  
User Data ◽  
Low Dimensional ◽  
And Behavior

Automated representation learning is behind many recent success stories in machine learning. It is often used to transfer knowledge learned from a large dataset (e.g., raw text) to tasks for which only a small number of training examples are available. In this paper, we review recent advance in learning to represent social media users in low-dimensional embeddings. The technology is critical for creating high performance social media-based human traits and behavior models since the ground truth for assessing latent human traits and behavior is often expensive to acquire at a large scale. In this survey, we review typical methods for learning a unified user embeddings from heterogeneous user data (e.g., combines social media texts with images to learn a unified user representation). Finally we point out some current issues and future directions.

scholarly journals Impresión 3D como Herramienta Didáctica para la Enseñanza de Algunos Conceptos de Ingeniería y Diseño

Ingeniería ◽  
2018 ◽  
Vol 23 (1) ◽  
pp. 70 ◽  
Author(s):  
Edwin Blasnilo Rua Ramirez ◽  
Fernando Jimenez Diaz ◽  
German Andres Gutierrez Arias ◽  
Nelson Iván Villamizar
Keyword(s):  
Teaching And Learning ◽  
Mechanical Engineering ◽  
Large Scale ◽  
Low Cost ◽  
Cost Effective ◽  
3D Printer ◽  
Course Grades ◽  
Wide Range ◽  
Technical Specifications ◽  
Future Work

Context: 3D printing can be used for a wide range of tasks such as the design and testing of prototypes and finished products in a shorter time. In mechanical engineering, prototype designs are continuously generated in academic class activities and final coursework projects by students and teachers. However, students show limitations while understanding the abstract concepts represented with such designs.Method: Firstly, a large scale 3D printer with improved technical specifications compared to traditional market options and similar price, was fabricated. By means of free software and hardware tools and easy-to-obtain alternative manufacturing materials, it was possible to decrease its manufacturing and operating costs. Then a set of study cases utilising the 3D printer in three different subject classes were designed and tested with two cohorts of students of Mechanical Engineering programme.Results: It was feasible to fabricate a cost-effective and practical 3D printer for constructing prototypes and pieces that benefit teaching and learning concepts in engineering and design areas. The experiments carried out in three subjects of engineering courses with second-year students, showed a similar trend of improving the average course grades, as it was observed in two cohorts in different terms.Conclusions: This type of low cost 3D printer obtained academic advantages as a didactic tool for the learning process in engineering and design subjects. Future work will consider applying this tool to other courses and subjects to further evaluate its convenience and effectivity.

Building High Performance Explainable Machine Learning Models for Social Media-based Substance Use Prediction

2020 ◽  
Vol 29 (03n04) ◽  
pp. 2060009
Author(s):  
Tao Ding ◽  
Fatema Hasan ◽  
Warren K. Bickel ◽  
Shimei Pan
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Substance Use ◽  
Human Behavior ◽  
High Performance ◽  
Supervised Machine Learning ◽  
Learning Models ◽  
Wide Range ◽  
And Behavior ◽  
Machine Learning Models

Social media contain rich information that can be used to help understand human mind and behavior. Social media data, however, are mostly unstructured (e.g., text and image) and a large number of features may be needed to represent them (e.g., we may need millions of unigrams to represent social media texts). Moreover, accurately assessing human behavior is often difficult (e.g., assessing addiction may require medical diagnosis). As a result, the ground truth data needed to train a supervised human behavior model are often difficult to obtain at a large scale. To avoid overfitting, many state-of-the-art behavior models employ sophisticated unsupervised or self-supervised machine learning methods to leverage a large amount of unsupervised data for both feature learning and dimension reduction. Unfortunately, despite their high performance, these advanced machine learning models often rely on latent features that are hard to explain. Since understanding the knowledge captured in these models is important to behavior scientists and public health providers, we explore new methods to build machine learning models that are not only accurate but also interpretable. We evaluate the effectiveness of the proposed methods in predicting Substance Use Disorders (SUD). We believe the methods we proposed are general and applicable to a wide range of data-driven human trait and behavior analysis applications.

Witnessing in crisis contexts in the social media age: the case of the 2015 Tianjin blasts on Weibo

2019 ◽  
Vol 42 (5) ◽  
pp. 675-691 ◽  
Author(s):  
Xiaoping Wu ◽  
Martin Montgomery
Keyword(s):  
Social Media ◽  
Personal Experience ◽  
Large Scale ◽  
Chinese Characters ◽  
Industrial Accidents ◽  
Social Media Platform ◽  
Word And Image ◽  
Crisis Events ◽  
The Social ◽  
Media Platform

This study explores how the act of witnessing takes distinctive discursive shape in social media in the context of large-scale disasters or crisis events. Drawing upon a set of microblogs posted on China’s most widely used social media platform, Weibo, immediately after one of China’s most serious industrial accidents, this study shows how witnessing manifests itself through individual eyewitness accounts foregrounding personal experience but constrained within Weibo’s standard protocol of 140 Chinese characters. Often blending word and image, these accounts are examined using methodologies drawn from discourse analysis. On this basis, we argue that witnessing in social media in China not only demonstrates strong participatory, connective, and self-reflexive characteristics. It also opens up new possibilities for various patterns of meaning, which, when appropriated by other social media users, constitute a public testimony that can challenge official discourses of crisis in China.

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