Intersection of Resilience and COVID-19: Structural Topic Modelling and Word Embeddings from Reddit Titles (Preprint)

2021 ◽  
Author(s):  
Alejandro Garcia-Rudolph ◽  
Blanca Cegarra ◽  
Joan Sauri ◽  
John D. Kelleher ◽  
Katryna Cisek ◽  
...  
Keyword(s):  
Social Media ◽  
State Of The Art ◽  
R Package ◽  
Topic Modelling ◽  
Word Embeddings ◽  
Scientific Publications ◽  
Self Presentation ◽  
Semantic Coherence ◽  
Preliminary Validation ◽  
Media Platform

BACKGROUND Topic modeling and word embeddings’ studies of Twitter data related to COVID-19 are being extensively reported. Another social media platform that experienced a tremendous increase in new users and posts due to COVID-19 was Reddit, offering a much less explored alternative, especially the submissions’ titles, due to their format (≤ 300 characters) and content rules. The positivity of self-presentation on social media has an influence on both the quantity and quality of reactions (upvotes) from other social media contacts. OBJECTIVE 1) Expand on the concept of resilience identifying possible related topics considering their number of upvotes and its closest terms and 2) Associate specific emotions obtained from the state-of-the-art literature to their closest terms in order to relate such emotions to experienced situations. METHODS Reddit data were collected from pushshift.io, with the pushshiftr R package, data cleaning and preprocessing was performed using quanteda, tidyverse, tidytext R packages. A word2vec model (W2V) was trained using submissions’ titles, preliminary validation was performed using a subset of Mikolov’s analogies and a COVID-19 glossary. The W2V model was trained with the wordVectors R package. Main topics (represented as sets of words) using the number of upvotes as covariate were extracted using structural topic modelling (STM) with the spectral methos using the stm R package. Topics validation was performed using semantic coherence and exclusivity. Clusters were assessed using Dunn index. RESULTS We collected all 374,421 titles submitted by 104,351 different redditors to the r/Coronavirus subreddit between January 20th 2020 and 14th May 2021. We trained W2V and identified more than 20 valid analogies (e.g. doctor – hospital + teacher = school). We further validated W2V with representative terms extracted from a COVID-19 glossary, all closest terms retrieved by W2V were verified using state of the art publications. STM retrieved 20 topics (with 20 words each) ordered by their number of upvotes, we run W2V in a representative topic (addressing vaccines) and we used two terms as seeds leading to other related terms (represented using cluster analysis) that we validated using scientific publications. STM did not retrieve any topic containing the term “resilience”, it hardly appeared (less than 0.02%) in all titles. Nevertheless we identified several closest terms (e.g. wellbeing, roadmap) and combined terms (e.g. resilience and elderly, resilience and indigenous) as well as specific emotions that W2V related to lived experiences (e.g. the emotion of gratitude associated to applauses and balconies). CONCLUSIONS We applied for the first time the combination of STM and a word2vec model trained with a relatively small Coronavirus dataset of Reddit titles, leading to immediate and accurate terms that can be used to expand our knowledge on topics associated to the pandemic (e.g. vaccines) or specific aspects such as resilience.

scholarly journals Authors Online: Strategies for Digital Self Promotion

2021 ◽  
Author(s):  
Ashley Regimbal-Kung
Keyword(s):  
Social Media ◽  
Literature Review ◽  
Best Practices ◽  
Case Studies ◽  
Digital Marketing ◽  
Self Presentation ◽  
Social Media Platform ◽  
Media Platform ◽  
Online Strategies

This paper explored strategies of digital self-promotion for authors online through the investigation of emerging, independent self-published writers. This research provides best practices through those strategies to assist self-published writers in furthering their public profile in digital marketing. The literature review provides context in the online self-publishing environment, connecting with the audience; encouraging collaboration (produsage); adapting to the shifting publishing marketplace through self-presentation strategies (branding), and; bolstering two-way communication (market sensing). It also provides the basis for coding self-presentation themes in self-presentation. This research suggests that best practices can optimize the time that writers spend on marketing, not only to attract initial attention from publishers but at any stage in their career. This research gathers data and develops case studies of four self-published authors that use Wattpad, a social media platform for writers. It analyzes these authors’ strategies for self-promotion and measures their effectiveness through the level of engagement elicited from their fans. It develops best practices from these strategies. This research finds that digital self-promotional activities are successful if they are creative, unique and develop a community of fan followers. It is especially effective when authors reflect the interests of their target audience. It was also found these strategies helped develop the author’s branding for long-term effectiveness

scholarly journals An Automated Method To Enrich Consumer Health Vocabularies Using GloVe Word Embeddings and An Auxiliary Lexical Resource (Preprint)

2020 ◽  
Author(s):  
Mohammed Ibrahim ◽  
Susan Gauch ◽  
Omar Salman ◽  
Mohammed Alqahatani
Keyword(s):  
Social Media ◽  
Ground Truth ◽  
Consumer Health ◽  
Word Embeddings ◽  
Relative Improvement ◽  
Lexical Resource ◽  
Automated Method ◽  
Medical Terms ◽  
Media Platform ◽  
Novel Algorithms

BACKGROUND Clear language makes communication easier between any two parties. A layman may have difficulty communicating with a professional due to not understanding the specialized terms common to the domain. In healthcare, it is rare to find a layman knowledgeable in medical jargon which can lead to poor understanding of their condition and/or treatment. To bridge this gap, several professional vocabularies and ontologies have been created to map laymen medical terms to professional medical terms and vice versa. OBJECTIVE Many of the presented vocabularies are built manually or semi-automatically requiring large investments of time and human effort and consequently the slow growth of these vocabularies. In this paper, we present an automatic method to enrich laymen's vocabularies that has the benefit of being able to be applied to vocabularies in any domain. METHODS Our entirely automatic approach uses machine learning, specifically Global Vectors for Word Embeddings (GloVe), on a corpus collected from a social media healthcare platform to extend and enhance consumer health vocabularies (CHV). Our approach further improves the CHV by incorporating synonyms and hyponyms from the WordNet ontology. The basic GloVe and our novel algorithms incorporating WordNet were evaluated using two laymen datasets from the National Library of Medicine (NLM), Open-Access Consumer Health Vocabulary (OAC CHV) and MedlinePlus Healthcare Vocabulary. RESULTS The results show that GloVe was able to find new laymen terms with an F-score of 48.44%. Furthermore, our enhanced GloVe approach outperformed basic GloVe with an average F-score of 61%, a relative improvement of 25%. CONCLUSIONS This paper presents an automatic approach to enrich consumer health vocabularies using the GloVe word embeddings and an auxiliary lexical source, WordNet. Our approach was evaluated used a healthcare text downloaded from MedHelp.org, a healthcare social media platform using two standard laymen vocabularies, OAC CHV, and MedlinePlus. We used the WordNet ontology to expand the healthcare corpus by including synonyms, hyponyms, and hypernyms for each CHV layman term occurrence in the corpus. Given a seed term selected from a concept in the ontology, we measured our algorithms’ ability to automatically extract synonyms for those terms that appeared in the ground truth concept. We found that enhanced GloVe outperformed GloVe with a relative improvement of 25% in the F-score.

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 Authors Online: Strategies for Digital Self Promotion

2021 ◽  
Author(s):  
Ashley Regimbal-Kung
Keyword(s):  
Social Media ◽  
Literature Review ◽  
Best Practices ◽  
Case Studies ◽  
Digital Marketing ◽  
Self Presentation ◽  
Social Media Platform ◽  
Media Platform ◽  
Online Strategies

This paper explored strategies of digital self-promotion for authors online through the investigation of emerging, independent self-published writers. This research provides best practices through those strategies to assist self-published writers in furthering their public profile in digital marketing. The literature review provides context in the online self-publishing environment, connecting with the audience; encouraging collaboration (produsage); adapting to the shifting publishing marketplace through self-presentation strategies (branding), and; bolstering two-way communication (market sensing). It also provides the basis for coding self-presentation themes in self-presentation. This research suggests that best practices can optimize the time that writers spend on marketing, not only to attract initial attention from publishers but at any stage in their career. This research gathers data and develops case studies of four self-published authors that use Wattpad, a social media platform for writers. It analyzes these authors’ strategies for self-promotion and measures their effectiveness through the level of engagement elicited from their fans. It develops best practices from these strategies. This research finds that digital self-promotional activities are successful if they are creative, unique and develop a community of fan followers. It is especially effective when authors reflect the interests of their target audience. It was also found these strategies helped develop the author’s branding for long-term effectiveness

scholarly journals A Text Mining Based Map of Engineering Design: Topics and their Trajectories Over Time

2019 ◽  
Vol 1 (1) ◽  
pp. 2765-2774 ◽  
Author(s):  
Filippo Chiarello ◽  
Nicola Melluso ◽  
Andrea Bonaccorsi ◽  
Gualtiero Fantoni
Keyword(s):  
Text Mining ◽  
Engineering Design ◽  
State Of The Art ◽  
Topic Modelling ◽  
Specific Topic ◽  
Scientific Publications ◽  
New Knowledge ◽  
New Research ◽  
Large Corpus ◽  
Over Time

AbstractThe Engineering Design field is growing fast and so is growing the number of sub-fields that are bringing value to researchers that are working in this context. From psychology to neurosciences, from mathematics to machine learning, everyday scholars and practitioners produce new knowledge of potential interest for designers.This leads to complications in the researchers’ aims who want to quickly and easily find literature on a specific topic among a large number of scientific publications or want to effectively position a new research.In the present paper, we address this problem by using state of the art text mining techniques on a large corpus of Engineering Design related documents. In particular, a topic modelling technique is applied to all the papers published in the ICED proceedings from 2003 to 2017 (3,129 documents) in order to find the main subtopics of Engineering Design. Finally, we analyzed the trends of these topics over time, to give a bird-eye view of how the Engineering Design field is evolving.The results offer a clear and bottom-up picture of what Engineering design is and how the interest of researchers in different topics has changed over time.

scholarly journals Strategi presentasi diri pengguna instagram ditinjau dari tipe kepribadian

2019 ◽  
Vol 7 (2) ◽  
Author(s):  
Devia Balqis Rarasati ◽  
Hudaniah Hudaniah ◽  
Susanti Prasetyaningrum
Keyword(s):  
Social Media ◽  
Real Life ◽  
Sampling Technique ◽  
Personality Inventory ◽  
T Test ◽  
Self Presentation ◽  
Average Value ◽  
Eysenck Personality Inventory ◽  
Presentation Strategy ◽  
Media Platform

Abstrak. Instagram merupakan salah satu tempat untuk presentasi diri. Presentasi diri yang berlebihan di instagram dapat membentuk identitas berbeda antara di dunia nyata dan online. Ada berbagai strategi dalam presentasi diri di media sosial atau bisa disebut strategi presentasi diri. Presentasi diri pengguna instagram memiliki tujuan berbeda. Hipotesis penelitan ini untuk mengetahui perbedaan strategi presentasi diri pengguna instagram antara tipe kepribadian ekstrovert dan introvert. Penelitian ini dilakukan pada 212 orang pengguna instagram berusia 18-24 tahun. Teknik pengambilan sampel yang digunakan yaitu menggunakan teknik non-probality sampling khususnya accidental sampling. Variabel peneltian menggunakan skala Self-Presentation Strategies via SNS dan Eysenck Personality Inventory A. Hasil perhitungan menggunakan teknik independent t-test ada perbedaan strategi presentasi diri pengguna instagram ekstrovert dan introvert. Pengguna instagram tipe kepribadian ekstrovert memiliki rata-rata lebih tinggi di bandingkan introvert (ingratiation M=10,015, supplication M=5,636, dan enhancement M=10,88). Namun, pada strategi presentasi diri ingratiation dan supplication  tidak ditemukan perbedaan yang signifikan.Kata kunci: Instagram, Strategi presentasi diri, Tipe kepribadian Abstract. Instagram as social media platform has many functions, one of which is for self-presentation. An excessive self-presentation on Instagram may result in the formation different identity between real life and online. There are various strategies in self-presentation on social media which can be referred as self-presentation strategy. The self-presentation on instagram has different purposes. This study aims at finding the different of self-presentation strategy between extrovert and introvert. There were 212 instagram users aged 18-24 years old as the subjects of the study. Sampling technique was the sampling non-probability technique, with particular of accidental sampling. The variable were measured using Self-Presentation Strategies scale via SNS and Eysenck Personality Inventory A. The calculation result which used the independent t-test showed there was a different in using self-presentation strategy between the extrovert and introvert. The extrovert users had relative higher average value than the introvert ones (Ingratiation M=10.015, Supplication M=5.636, and Enhancement M=10.88). Yet, there was no significant different in self-presentation strategy between ingratiation and supplication.Keywords : Instagram, Personality type, Self presentation strategies

scholarly journals Deep learning for pharmacovigilance: recurrent neural network architectures for labeling adverse drug reactions in Twitter posts

2017 ◽  
Vol 24 (4) ◽  
pp. 813-821 ◽  
Author(s):  
Anne Cocos ◽  
Alexander G Fiks ◽  
Aaron J Masino
Keyword(s):  
Neural Network ◽  
Social Media ◽  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Recurrent Neural Network ◽  
State Of The Art ◽  
Detection Performance ◽  
Word Embeddings

Abstract Objective Social media is an important pharmacovigilance data source for adverse drug reaction (ADR) identification. Human review of social media data is infeasible due to data quantity, thus natural language processing techniques are necessary. Social media includes informal vocabulary and irregular grammar, which challenge natural language processing methods. Our objective is to develop a scalable, deep-learning approach that exceeds state-of-the-art ADR detection performance in social media. Materials and Methods We developed a recurrent neural network (RNN) model that labels words in an input sequence with ADR membership tags. The only input features are word-embedding vectors, which can be formed through task-independent pretraining or during ADR detection training. Results Our best-performing RNN model used pretrained word embeddings created from a large, non–domain-specific Twitter dataset. It achieved an approximate match F-measure of 0.755 for ADR identification on the dataset, compared to 0.631 for a baseline lexicon system and 0.65 for the state-of-the-art conditional random field model. Feature analysis indicated that semantic information in pretrained word embeddings boosted sensitivity and, combined with contextual awareness captured in the RNN, precision. Discussion Our model required no task-specific feature engineering, suggesting generalizability to additional sequence-labeling tasks. Learning curve analysis showed that our model reached optimal performance with fewer training examples than the other models. Conclusions ADR detection performance in social media is significantly improved by using a contextually aware model and word embeddings formed from large, unlabeled datasets. The approach reduces manual data-labeling requirements and is scalable to large social media datasets.

scholarly journals Understanding Media Communication: On the Significance of Iconic Thinking for a Praxeological Model of Communication

SAGE Open ◽  
2020 ◽  
Vol 10 (3) ◽  
pp. 215824402095206
Author(s):  
Aglaja Przyborski ◽  
Thomas Slunecko
Keyword(s):  
Qualitative Research ◽  
Social Media ◽  
Social Research ◽  
State Of The Art ◽  
The State ◽  
Self Presentation ◽  
Media Communication ◽  
Picture Analysis

This article outlines the state of the art in picture analysis as it has been developed in the trajectory of reconstructive methodology. Analyzing pictures in their own right—that is, by adhering to the particular affordances of the medium “picture”—has strong implications for qualitative research some of which are discussed in this article. With regard to content, this discussion revolves around questions pertaining to bodily self-presentation in mass and social media. On this basis, the article concludes with a praxeological model of communication that offers a guideline for social research which is clued-up as to its own media and, thus, takes into account that meaning in pictures is constructed differently than meaning in language.

scholarly journals SEAMLESS AND SECURE INTEGRATION OF SOCIALMEDIA, E-PORTFOLIO AND ALUMN SERVICES INTO UNIVERSITY INFORMATION ARCHITECTURE

2014 ◽  
pp. 106-115
Author(s):  
Ontje Helmich ◽  
Michael A. Herzog ◽  
Christian Neumann
Keyword(s):  
Higher Education ◽  
Social Media ◽  
Web Applications ◽  
State Of The Art ◽  
Information Architecture ◽  
It Infrastructure ◽  
Social Media Platform ◽  
Information Portal ◽  
Media Platform ◽  
User Friendly

This document describes the concept and prototype for an “assembling” IT intergration portal to be used in higher education. Through the use of profiles, good usability and display of concentrated information students should be bound to the portal and university throughout their studies and their life. The proposed solution, to use Elgg as an information portal and social media platform, bridges the gap between the closed nature of university IT infrastructure and user-friendly, communication enhancing advancements of state-of-the-art web applications.

scholarly journals Privacy and Social Media: Defining Privacy in the Usage of Path

2019 ◽  
Author(s):  
Mashita Phitaloka Fandia Purwaningtyas
Keyword(s):  
Social Media ◽  
Personal Information ◽  
Personal Space ◽  
Social Recognition ◽  
Reciprocal Relationship ◽  
The Self ◽  
Self Presentation ◽  
Space Construction ◽  
Media Platform ◽  
Definition Of

The existence of social media has changed the landscape of human’s relationship. Through social media, people are able to present many versions of themselves in many platforms. In this era of polymediation of the self, the discussion regarding to privacy becomes arguable, moreover, with the presence of Path; a social media platform which presents itself as a private social media. Hence, in the sociocultural context of Indonesian society, it is important to see how the definition of privacy is constructed by the existence of Path. Therefore, this research is conducted in order to analyze and explore how privacy is perceived by the social media users nowadays, particularly the users of Path, and why they perceive it in that certain way. This research is conducted with ethnography as the main method and virtual ethnography as the supporting method. From the research, it is found that users’ way of defining privacy is embodied in two levels: online self-presentation and personal space construction. In the first level, the stages of privacy offered by Path have created the fragmented-self among users. This fragmentation has resulted in “the ambivalent self”, “self that desires recognition”, and “self that searches for freedom”. In the second level, the mediality of Path has served the users of the ability to construct their own personal space in social media space. This construction of the personal space has resulted in “space of comfort in similarity”, “space of pseudo-liberation”, and “space that demolishes the panoptic”. Henceforth, these findings lead to a conclusion that usage practices of social media has killed the authentic self and created a personal space that gives the sense of the absence of control, hierarchy, and social surveillance. Eventually, privacy for Path is defined by the process of exchange of “the self and personal information” with “social recognition, sense of equality, and reciprocal relationship”.

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