scholarly journals Social media and deep learning capture the aesthetic quality of the landscape

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ilan Havinga ◽  
Diego Marcos ◽  
Patrick W. Bogaart ◽  
Lars Hein ◽  
Devis Tuia
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Large Scale ◽  
Survey Methods ◽  
Well Being ◽  
Point Of View ◽  
Aesthetic Quality ◽  
Large Scale Analysis ◽  
Aesthetic Enjoyment ◽  
The Aesthetic

AbstractPeoples’ recreation and well-being are closely related to their aesthetic enjoyment of the landscape. Ecosystem service (ES) assessments record the aesthetic contributions of landscapes to peoples’ well-being in support of sustainable policy goals. However, the survey methods available to measure these contributions restrict modelling at large scales. As a result, most studies rely on environmental indicator models but these do not incorporate peoples’ actual use of the landscape. Now, social media has emerged as a rich new source of information to understand human-nature interactions while advances in deep learning have enabled large-scale analysis of the imagery uploaded to these platforms. In this study, we test the accuracy of Flickr and deep learning-based models of landscape quality using a crowdsourced survey in Great Britain. We find that this novel modelling approach generates a strong and comparable level of accuracy versus an indicator model and, in combination, captures additional aesthetic information. At the same time, social media provides a direct measure of individuals’ aesthetic enjoyment, a point of view inaccessible to indicator models, as well as a greater independence of the scale of measurement and insights into how peoples’ appreciation of the landscape changes over time. Our results show how social media and deep learning can support significant advances in modelling the aesthetic contributions of ecosystems for ES assessments.

scholarly journals An experimental study on feature engineering and learning approaches for aggression detection in social media

2019 ◽  
Vol 22 (63) ◽  
pp. 81-100 ◽  
Author(s):  
Antonela Tommasel ◽  
Juan Manuel Rodriguez ◽  
Daniela Godoy
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Social Relations ◽  
Large Scale ◽  
Well Being ◽  
Feature Engineering ◽  
Learning Approaches ◽  
Social Media Networks ◽  
Online Social Media ◽  
Learning Techniques

With the widespread of modern technologies and social media networks, a new form of bullying occurring anytime and anywhere has emerged. This new phenomenon, known as cyberaggression or cyberbullying, refers to aggressive and intentional acts aiming at repeatedly causing harm to other person involving rude, insulting, offensive, teasing or demoralising comments through online social media. As these aggressions represent a threatening experience to Internet users, especially kids and teens who are still shaping their identities, social relations and well-being, it is crucial to understand how cyberbullying occurs to prevent it from escalating. Considering the massive information on the Web, the developing of intelligent techniques for automatically detecting harmful content is gaining importance, allowing the monitoring of large-scale social media and the early detection of unwanted and aggressive situations. Even though several approaches have been developed over the last few years based both on traditional and deep learning techniques, several concerns arise over the duplication of research and the difficulty of comparing results. Moreover, there is no agreement regarding neither which type of technique is better suited for the task, nor the type of features in which learning should be based. The goal of this work is to shed some light on the effects of learning paradigms and feature engineering approaches for detecting aggressions in social media texts. In this context, this work provides an evaluation of diverse traditional and deep learning techniques based on diverse sets of features, across multiple social media sites. 

Concrete Monsters of the Welfare State: Discussions of Brutalist Architecture on Social Media

2021 ◽  
pp. 120633122110193
Author(s):  
Max Holleran
Keyword(s):  
Social Media ◽  
Welfare State ◽  
Large Scale ◽  
Eastern European ◽  
Municipal Governments ◽  
Online Social Media ◽  
1960S And 1970S ◽  
The 1960S ◽  
The Aesthetic ◽  
The Welfare State

Brutalist architecture is an object of fascination on social media that has taken on new popularity in recent years. This article, drawing on 3,000 social media posts in Russian and English, argues that the buildings stand out for their arresting scale and their association with the expanding state in the 1960s and 1970s. In both North Atlantic and Eastern European contexts, the aesthetic was employed in publicly financed urban planning projects, creating imposing concrete structures for universities, libraries, and government offices. While some online social media users associate the style with the overreach of both socialist and capitalist governments, others are more nostalgic. They use Brutalist buildings as a means to start conversations about welfare state goals of social housing, free university, and other services. They also lament that many municipal governments no longer have the capacity or vision to take on large-scale projects of reworking the built environment to meet contemporary challenges.

SHEDR: An End-to-End Deep Neural Event Detection and Recommendation Framework for Hyperlocal News Using Social Media

2021 ◽  
Author(s):  
Yuheng Hu ◽  
Yili Hong
Keyword(s):  
Neural Network ◽  
Social Media ◽  
Deep Learning ◽  
Event Detection ◽  
Large Scale ◽  
Short Term Memory ◽  
State Of The Art ◽  
Neural Network Models ◽  
Neural Event ◽  
End To End

Residents often rely on newspapers and television to gather hyperlocal news for community awareness and engagement. More recently, social media have emerged as an increasingly important source of hyperlocal news. Thus far, the literature on using social media to create desirable societal benefits, such as civic awareness and engagement, is still in its infancy. One key challenge in this research stream is to timely and accurately distill information from noisy social media data streams to community members. In this work, we develop SHEDR (social media–based hyperlocal event detection and recommendation), an end-to-end neural event detection and recommendation framework with a particular use case for Twitter to facilitate residents’ information seeking of hyperlocal events. The key model innovation in SHEDR lies in the design of the hyperlocal event detector and the event recommender. First, we harness the power of two popular deep neural network models, the convolutional neural network (CNN) and long short-term memory (LSTM), in a novel joint CNN-LSTM model to characterize spatiotemporal dependencies for capturing unusualness in a region of interest, which is classified as a hyperlocal event. Next, we develop a neural pairwise ranking algorithm for recommending detected hyperlocal events to residents based on their interests. To alleviate the sparsity issue and improve personalization, our algorithm incorporates several types of contextual information covering topic, social, and geographical proximities. We perform comprehensive evaluations based on two large-scale data sets comprising geotagged tweets covering Seattle and Chicago. We demonstrate the effectiveness of our framework in comparison with several state-of-the-art approaches. We show that our hyperlocal event detection and recommendation models consistently and significantly outperform other approaches in terms of precision, recall, and F-1 scores. Summary of Contribution: In this paper, we focus on a novel and important, yet largely underexplored application of computing—how to improve civic engagement in local neighborhoods via local news sharing and consumption based on social media feeds. To address this question, we propose two new computational and data-driven methods: (1) a deep learning–based hyperlocal event detection algorithm that scans spatially and temporally to detect hyperlocal events from geotagged Twitter feeds; and (2) A personalized deep learning–based hyperlocal event recommender system that systematically integrates several contextual cues such as topical, geographical, and social proximity to recommend the detected hyperlocal events to potential users. We conduct a series of experiments to examine our proposed models. The outcomes demonstrate that our algorithms are significantly better than the state-of-the-art models and can provide users with more relevant information about the local neighborhoods that they live in, which in turn may boost their community engagement.

THE ROLE OF URBAN AESTHETICS ON ENHANCING VITALITY OF URBAN SPACES

2021 ◽  
pp. xx-xx
Keyword(s):  
Urban Design ◽  
Direct Consequence ◽  
Point Of View ◽  
Urban Spaces ◽  
Urban Setting ◽  
Aesthetic Quality ◽  
Aesthetic Design ◽  
Active Involvement ◽  
The Aesthetic

Several scholars have focused on the different approaches in designing convivial urban spaces, but literary evidence shows that the essence of aesthetic design in public urban spaces, by referring to the main dimensions involved in the shaping of urban vitality, has not been adequately researched. In this regard, this study, by hypothesizing that the quality of urban design leads to a vital urban environment, focuses on urban vitality from the aesthetic point of view. Thus, in using qualitative grounded theory as a main methodological tool and using a systematic review of the related literature as the main induction approach for collecting qualitative data, five main dimensions of urban vitality, which are necessary to attain a correlation with the aesthetic quality of urban design, were conceptualized. The study concludes that the aesthetic design of an urban setting has a direct effect on the active involvement of its users and that this, therefore, has a direct consequence on the level of public urban vitality, manifested. Integrating the complexity theory with the five main dimensions used for assessing urban vitality was suggested as a viable area for further research.

scholarly journals Loodus tuleb tagasi: delfiinid ja saurused. Libauudistest, photoshop-muistenditest ja meemidest koroonakarantiini ajal

Mäetagused ◽  
2021 ◽  
Vol 79 ◽  
pp. 167-184
Author(s):  
Eda Kalmre ◽  
Keyword(s):  
Social Media ◽  
Coping Strategies ◽  
Well Being ◽  
Point Of View ◽  
Human Existence ◽  
The Other ◽  
Fake News ◽  
The Social ◽  
Truth Value ◽  
Positive Effect

The article follows the narrative trend initiated by the social media posts and fake news during the first months of the corona quarantine, which claims that the decrease of contamination due to the quarantine has a positive effect on the environment and nature recovery. The author describes the context of the topic and follows the changes in the rhetoric through different genres, discussing the ways in which a picture can tell a truthful story. What is the relation between the context, truth, and rhetoric? This material spread globally, yet it was also readily “translated” into the Estonian context, and – what is very characteristic of the entire pandemic material – when approaching this material, truthful and fabricated texts, photos, and videos were combined. From the folkloristic point of view, these rumours in the form of fake news, first presented in the function of a tall tale and further following the sliding truth scale of legends, constitute a part of coping strategies, so-called crisis humour, yet, on the other hand, also a belief story presenting positive imagery, which surrounds the mainly apocalyptically perceived pandemic period and interprets the human existence on a wider scale. Even if these fake news and memes have no truth value, they communicate an idea – nature recovers – and definitely offer hope and a feeling of well-being.

The Role of Social Media in Event Marketing

2021 ◽  
pp. 262-281
Author(s):  
Serap Serin Karacaer
Keyword(s):  
Social Media ◽  
Large Scale ◽  
Service Marketing ◽  
Target Audience ◽  
Point Of View ◽  
Event Marketing ◽  
Effective Current ◽  
Marketing Activities ◽  
Effective Transfer

Activities, which include events that are not all intangible, include large-scale service components, and hence, their marketing includes service marketing. From this point of view, it is possible to state that it is very difficult to market activities that the participants cannot take home and consume physically. In this context, it is very important that the event marketing activities convey the feeling to the target audience that they will have fun and be entertained. Therefore, social media is one of the most important tools used in the effective transfer of the organization to the target audience within the scope of event marketing activities. As the most effective current communication and interaction tool, social media has become the most important tool for event marketers who are trying to appeal to large audiences and promote a certain destination, product, or service.

scholarly journals A Deep Learning-Based Approach for Identifying the Medicinal Uses of Plant-Derived Natural Compounds

2020 ◽  
Vol 11 ◽  
Author(s):  
Sunyong Yoo ◽  
Hyung Chae Yang ◽  
Seongyeong Lee ◽  
Jaewook Shin ◽  
Seyoung Min ◽  
...  
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Natural Compound ◽  
Biological Activities ◽  
Natural Compounds ◽  
Model Potential ◽  
Medicinal Uses ◽  
Large Scale Analysis ◽  
Investigational Drugs

Medicinal plants and their extracts have been used as important sources for drug discovery. In particular, plant-derived natural compounds, including phytochemicals, antioxidants, vitamins, and minerals, are gaining attention as they promote health and prevent disease. Although several in vitro methods have been developed to confirm the biological activities of natural compounds, there is still considerable room to reduce time and cost. To overcome these limitations, several in silico methods have been proposed for conducting large-scale analysis, but they are still limited in terms of dealing with incomplete and heterogeneous natural compound data. Here, we propose a deep learning-based approach to identify the medicinal uses of natural compounds by exploiting massive and heterogeneous drug and natural compound data. The rationale behind this approach is that deep learning can effectively utilize heterogeneous features to alleviate incomplete information. Based on latent knowledge, molecular interactions, and chemical property features, we generated 686 dimensional features for 4,507 natural compounds and 2,882 approved and investigational drugs. The deep learning model was trained using the generated features and verified drug indication information. When the features of natural compounds were applied as input to the trained model, potential efficacies were successfully predicted with high accuracy, sensitivity, and specificity.

scholarly journals The performance of publicness in social media: tracing patterns in tweets after a disaster

2017 ◽  
Vol 40 (4) ◽  
pp. 584-599 ◽  
Author(s):  
Donald Matheson
Keyword(s):  
Social Media ◽  
Large Scale ◽  
Public Communication ◽  
Stable Set ◽  
Computer Assisted ◽  
Social Imaginary ◽  
Media Communication ◽  
Aotearoa New Zealand ◽  
Large Scale Analysis ◽  
The Social

This article sets out to contribute to the critical understanding of public communication in social media by studying the use of Twitter after a severe earthquake in Aotearoa New Zealand in 2011. It also sets out to contribute to methodologies for studying this particular kind of publicness. It argues that the contours of the ‘social imaginary’ of the public, which are usually so hard to delineate and can be approached only in fragments or typical form, can be identified a little more clearly in the traces that people leave behind in their social media communication at critical, reflexive moments such as in the aftermath of disaster. The article draws on computer-assisted discourse analysis, specifically a corpus-linguistic-informed analysis of half a million tweets, in order to describe four main public discursive moves that were prevalent in this form of public communication. This is not to claim to describe a stable set of norms, but in fact the reverse. The article suggests that empirical, large-scale analysis of public communication in different situations, media and places opens up a project in which the varying norms of public communication are described and critiqued as they emerge in a range of discursive situations.

scholarly journals Natural Language Processing of Medical Alert Service Notes Reveals Reasons for Emergency Admissions

Iproceedings ◽  
10.2196/15225 ◽  
2019 ◽  
Vol 5 (1) ◽  
pp. e15225
Author(s):  
Felipe Masculo ◽  
Jorn op den Buijs ◽  
Mariana Simons ◽  
Aki Harma
Keyword(s):  
Deep Learning ◽  
Chest Pain ◽  
Language Processing ◽  
Large Scale ◽  
Emergency Situation ◽  
Scale Analysis ◽  
Vector Representation ◽  
Large Scale Analysis ◽  
Structured Information ◽  
Insight Into

Background A Personal Emergency Response Service (PERS) enables an aging population to receive help quickly when an emergency situation occurs. The reasons that trigger a PERS alert are varied, including a sudden worsening of a chronic condition, a fall, or other injury. Every PERS case is documented by the response center using a combination of structured variables and free text notes. The text notes, in particular, contain a wealth of information in case of an incident such as contextual information, details about the situation, symptoms and more. Analysis of these notes at a population level could provide insight into the various situations that cause PERS medical alerts. Objective The objectives of this study were to (1) develop methods to enable the large-scale analysis of text notes from a PERS response center, and (2) to apply these methods to a large dataset and gain insight into the different situations that cause medical alerts. Methods More than 2.5 million deidentified PERS case text notes were used to train a document embedding model (ie, a deep learning Recurrent Neural Network [RNN] that takes the medical alert text note as input and produces a corresponding fixed length vector representation as output). We applied this model to 100,000 PERS text notes related to medical incidents that resulted in emergency department admission. Finally, we used t-SNE, a nonlinear dimensionality reduction method, to visualize the vector representation of the text notes in 2D as part of a graphical user interface that enabled interactive exploration of the dataset and visual analytics. Results Visual analysis of the vectors revealed the existence of several well-separated clusters of incidents such as fall, stroke/numbness, seizure, breathing problems, chest pain, and nausea, each of them related to the emergency situation encountered by the patient as recorded in an existing structured variable. In addition, subclusters were identified within each cluster which grouped cases based on additional features extracted from the PERS text notes and not available in the existing structured variables. For example, the incidents labeled as falls (n=37,842) were split into several subclusters corresponding to falls with bone fracture (n=1437), falls with bleeding (n=4137), falls caused by dizziness (n=519), etc. Conclusions The combination of state-of-the-art natural language processing, deep learning, and visualization techniques enables the large-scale analysis of medical alert text notes. This analysis demonstrates that, in addition to falls alerts, the PERS service is broadly used to signal for help in situations often related to underlying chronic conditions and acute symptoms such as respiratory distress, chest pain, diabetic reaction, etc. Moreover, the proposed techniques enable the extraction of structured information related to the medical alert from unstructured text with minimal human supervision. This structured information could be used, for example, to track trends over time, to generate concise medical alert summaries, and to create predictive models for desired outcomes.

scholarly journals MILLENNIAL MOMS: THE INFLUENCE OF FATHER INVOLVEMENT TO MARITAL WELL BEING AND ITS IMPACT ON EMOTIONAL SELF-DISCLOSURE IN SOCIAL MEDIA

2020 ◽  
Vol 8 (4) ◽  
pp. 596-607
Author(s):  
Yuanita Setyastuti ◽  
Jenny Ratna Seminar ◽  
Purwanti Hadisiwi ◽  
Feliza Zubair
Keyword(s):  
Social Media ◽  
Marital Conflict ◽  
Father Involvement ◽  
Online Survey ◽  
Survey Methods ◽  
Well Being ◽  
Self Disclosure ◽  
Household Tasks ◽  
Household Task ◽  
New Evidence

Purpose of the study: This study aims to describe the influence of millennial moms' perceptions about father involvement of parenting and household tasks to her marital well-being and its impact on her emotional self-disclosure (ESD) about parenting in social media. Methodology: This study was a quantitative approach to online survey methods. The subjects are moms born in 1978-1994, have young children and social media users. The online survey distributed to 450 millennial mothers used Emotional Self Disclosure (ESD) Scale and Marital Well Being scale, including Marital Satisfaction, Marital Conflict, Parenting Stress, and Depression. Data analysis used path analysis through Smart PLS. Main Findings: The results show that mother perception of the father's involvement influences millennial moms' marital well-being and impact on Millennial moms' Emotional Self Disclosure (ESD) about Parenting in Social Media. The higher the millennial moms' perception of the father's involvement in parenting and household tasks, the higher their marital well-being. The higher the millennial mom's marital well-being, the less their Emotional Self Disclosure (ESD) about Parenting in Social Media. Applications of this study: This study is important and useful because it shows how important a husband's involvement in family so millennial family could escalate husband involvement in parenting and household task to maintain the marital well-being. This result also proves the importance of managing social media content because it can indicate marital well being. Novelty/Originality of this study: The findings of this study provide new evidence that emotional self-disclosure shown on social media suggests a person's marital well-being. Also new provide that mother perceptions about husband's involvement influence marital well-being.

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