Exploring Visual Representation of #ShoutYourAbortion Hashtag Movement and the Public’s Responses on Instagram: Descriptive and Infodemiology Study (Preprint)

2021 ◽  
Author(s):  
Sunmi ‍Lee ◽  
Yunhwan Kim
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Social Media ◽  
Neural Network Model ◽  
Predictive Analytics ◽  
Visual Representation ◽  
Point Of View ◽  
The Public ◽  
Intelligence Services ◽  
Human Faces

BACKGROUND Hashtag movement has become one of the major ways of online movement, but few studies have examined how social media photos were used for the movement. Also, it has not been actively investigated how photo features were related to the public’s responses in hashtag movements. OBJECTIVE The aim of the present research was to explore Instagram photos with #ShoutYourAbortion hashtag, as an example of hashtag movements via photos, in terms of their visual representation and the relationships between photo features and the public’s responses to the photos. METHODS Instagram photos with #ShoutYourAbortion hashtag, 11,176 in total, were downloaded, and their content and embedded texts were analyzed using online artificial intelligence services. The photos were clustered into subgroups based on the features extracted using a pretrained convolutional neural network model. The resulting clusters were compared in terms of their content tags, embedded texts, and photo features which were manually extracted at the content and pixel levels. The public’s responses were measured by engagement and comment sentiment. Correlational analysis and predictive analytics were conducted to examine the relationships between photo features and the public’s responses. RESULTS It was found that the photos in the text category took the largest share (57.19%), and the embedded texts were mainly about stories told in first person point of view as a woman. A possible evidence of hashtag hijacking was observed. The photos were grouped into two clusters; the first cluster comprised photos which exhibit text materials on them, while the second cluster consisted of photos which contain human faces with texts. The photos in the first cluster were brighter, while the photos in the second cluster were more colorful than the others. And public responses were found to be related to photo features such as size of faces, happy emotion, and share of warm colors. Engagement was predicted from the photo features with an acceptable level of accuracy, while comment sentiment was not. CONCLUSIONS This This study has shown the visual representation of #ShoutYourAbortion hashtag movement. It has also shown how photo features at content and pixel levels were related to the public’s responses to the photos. The results are expected to contribute to the understanding of hashtag movements via photos and making photos in hashtag movements more appealing to the public. CLINICALTRIAL Not Applicable

New Wars and Their Visual Representation: Dead Bodies without Graves/Mourne

2020 ◽  
Vol 2 ◽  
pp. 1-18
Author(s):  
Tuncay Şur ◽  
Betül Yarar
Keyword(s):  
Social Media ◽  
Visual Representation ◽  
Human Beings ◽  
The Public ◽  
Photographic Images ◽  
War Photography ◽  
New Wars ◽  
The Social ◽  
History Of ◽  
The Impact

This paper seeks to understand why there has been an increase in photographic images exposing military violence or displaying bodies killed by military forces and how they can freely circulate in the public without being censored or kept hidden. In other words, it aims to analyze this particular issue as a symptom of the emergence of new wars and a new regime of their visual representation. Within this framework, it attempts to relate two kinds of literature that are namely the history of war and war photography with the bridge of theoretical discussions on the real, its photographic representation, power, and violence.  Rather than systematic empirical analysis, the paper is based on a theoretical attempt which is reflected on some socio-political observations in the Middle East where there has been ongoing wars or new wars. The core discussion of the paper is supported by a brief analysis of some illustrative photographic images that are served through the social media under the circumstances of war for instance in Turkey between Turkish military troops and the Kurdish militants. The paper concludes that in line with the process of dissolution/transformation of the old nation-state formations and globalization, the mechanism and mode of power have also transformed to the extent that it resulted in the emergence of new wars. This is one dynamic that we need to recognize in relation to the above-mentioned question, the other is the impact of social media in not only delivering but also receiving war photographies. Today these changes have led the emergence of new machinery of power in which the old modern visual/photographic techniques of representing wars without human beings, torture, and violence through censorship began to be employed alongside medieval power techniques of a visual exhibition of tortures and violence.

scholarly journals Cloud based mobile solution for early detection of Skin Cancer using Artificial Intelligence

2021 ◽  
pp. 312-324
Author(s):  
Pawan Sonawane ◽  
Sahel Shardhul ◽  
Raju Mendhe
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Skin Cancer ◽  
Early Detection ◽  
Transfer Learning ◽  
Network Model ◽  
Neural Network Model ◽  
Mobile Application ◽  
Low Cost ◽  
Skin Tumors

The vast majority of skin cancer deaths are from melanoma, with about 1.04 million cases annually. Early detection of the same can be immensely helpful in order to try to cure it. But most of the diagnosis procedures are either extremely expensive or not available to a vast majority, as these centers are concentrated in urban regions only. Thus, there is a need for an application that can perform a quick, efficient, and low-cost diagnosis. Our solution proposes to build a server less mobile application on the AWS cloud that takes the images of potential skin tumors and classifies it as either Malignant or Benign. The classification would be carried out using a trained Convolution Neural Network model and Transfer learning (Inception v3). Several experiments will be performed based on Morphology and Color of the tumor to identify ideal parameters.

Discovery of Sustainable Transport Modes Underlying TripAdvisor Reviews With Sentiment Analysis

2021 ◽  
pp. 180-199
Author(s):  
Ainhoa Serna ◽  
Jon Kepa Gerrikagoitia
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Predictive Analytics ◽  
Data Gathering ◽  
Point Of View ◽  
Training Data ◽  
Complete Analysis ◽  
Sustainable Transport ◽  
Transport Modes

In recent years, digital technology and research methods have developed natural language processing for better understanding consumers and what they share in social media. There are hardly any studies in transportation analysis with TripAdvisor, and moreover, there is not a complete analysis from the point of view of sentiment analysis. The aim of study is to investigate and discover the presence of sustainable transport modes underlying in non-categorized TripAdvisor texts, such as walking mobility in order to impact positively in public services and businesses. The methodology follows a quantitative and qualitative approach based on knowledge discovery techniques. Thus, data gathering, normalization, classification, polarity analysis, and labelling tasks have been carried out to obtain sentiment labelled training data set in the transport domain as a valuable contribution for predictive analytics. This research has allowed the authors to discover sustainable transport modes underlying the texts, focused on walking mobility but extensible to other means of transport and social media sources.

scholarly journals Identification and Verification of Biomarker in Clear Cell Renal Cell Carcinoma via Bioinformatics and Neural Network Model

2020 ◽  
Vol 2020 ◽  
pp. 1-24
Author(s):  
Bin Liu ◽  
Yu Xiao ◽  
Hao Li ◽  
Ai-li Zhang ◽  
Ling-bing Meng ◽  
...  
Keyword(s):  
Neural Network ◽  
Renal Cell Carcinoma ◽  
Network Model ◽  
Neural Network Model ◽  
Clear Cell ◽  
Hub Genes ◽  
Cell Renal Cell Carcinoma ◽  
The Public ◽  
The Neural Network ◽  
Public Data

Background. Clear cell renal cell carcinoma (ccRCC) is the most common subtype of kidney cancer, which represents the 9th most frequently diagnosed cancer. However, the molecular mechanism of occurrence and development of ccRCC is indistinct. Therefore, the research aims to identify the hub biomarkers of ccRCC using numerous bioinformatics tools and functional experiments. Methods. The public data was downloaded from the Gene Expression Omnibus (GEO) database, and the differently expressed genes (DEGs) between ccRCC and normal renal tissues were identified with GEO2R. Protein-protein interaction (PPI) network of the DEGs was constructed, and hub genes were screened with cytoHubba. Then, ten ccRCC tumor samples and ten normal kidney tissues were obtained to verify the expression of hub genes with the RT-qPCR. Finally, the neural network model was constructed to verify the relationship among the genes. Results. A total of 251 DEGs and ten hub genes were identified. AURKB, CCNA2, TPX2, and NCAPG were highly expressed in ccRCC compared with renal tissue. With the increasing expression of AURKB, CCNA2, TPX2, and NCAPG, the pathological stage of ccRCC increased gradually (P<0.05). Patients with high expression of AURKB, CCNA2, TPX2, and NCAPG have a poor overall survival. After the verification of RT-qPCR, the expression of hub genes was same as the public data. And there were strong correlations between the AURKB, CCNA2, TPX2, and NCAPG with the verification of the neural network model. Conclusion. After the identification and verification, AURKB, CCNA2, TPX2, and NCAPG might be related to the occurrence and malignant progression of ccRCC.

scholarly journals Parodi Politik dalam Demokrasi Digital Studi Kasus: Akun Instagram Nurhadi-Aldo

PERSPEKTIF ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 36-46
Author(s):  
Bhakti Satrio Wicaksono
Keyword(s):  
Social Media ◽  
Participant Observation ◽  
Point Of View ◽  
Internet Technology ◽  
The Political ◽  
The Public ◽  
Digital Democracy ◽  
Political World ◽  
The Government ◽  
Observation Method

This article aims to find out what happened to digital democracy regarding the Dildo account case. This research uses descriptive qualitative research research methods. The type of observation method used is the non-participation observation method. The non-participant observation method is the method that researchers chose in conducting this research. The results of the study show that the view of digital democracy can be seen from two perspectives, namely the government and the people's point of view. Political parody basically offers a new way of engaging in the political world. The Dildo case shows that not everyone can freely express their opinion on social media. The freedom to voice opinions, especially through cyberspace, still cannot be said to be free. This can be seen with how when the Dildo account voices humor and national issues that are close to the public, it still gets contra opinions from other parties. The conclusion of the research is that political parody basically offers a new way of engaging in the political world. Dildo case shows that not everyone can freely express their opinion in social media. The existence of internet technology in terms of statehood can be a double-edged knife, especially on social media where information is spread so fast. Parody or current humor is treated wisely and is not necessarily considered a dangerous thing.

scholarly journals Legal AI from a Privacy Point of View: Data Protection and Transparency in Focus

2021 ◽  
pp. 181-202
Author(s):  
Cecilia Magnusson Sjöberg
Keyword(s):  
Artificial Intelligence ◽  
Data Processing ◽  
Success Factors ◽  
Personal Data ◽  
Point Of View ◽  
The Public ◽  
Critical Success ◽  
Starting Point ◽  
Access Rights ◽  
Digital Human

A major starting point is that transparency is a condition for privacy in the context of personal data processing, especially when based on artificial intelligence (AI) methods. A major keyword here is openness, which however is not equivalent to transparency. This is explained by the fact that an organization may very well be governed by principles of openness but still not provide transparency due to insufficient access rights and lacking implementation of those rights. Given these hypotheses, the chapter investigates and illuminates ways forward in recognition of algorithms, machine learning, and big data as critical success factors of personal data processing based on AI—that is, if privacy is to be preserved. In these circumstances, autonomy of technology calls for attention and needs to be challenged from a variety of perspectives. Not least, a legal approach to digital human sciences appears to be a resource to examine further. This applies, for instance, when data subjects in the public as well as in the private sphere are exposed to AI for better or for worse. Providing what may be referred to as a legal shield between user and application might be one remedy to shortcomings in this context.

scholarly journals Identifying User Profile by Incorporating Self-Attention Mechanism based on CSDN Data Set

2019 ◽  
Vol 1 (2) ◽  
pp. 160-175 ◽  
Author(s):  
Junru Lu ◽  
Le Chen ◽  
Kongming Meng ◽  
Fengyi Wang ◽  
Jun Xiang ◽  
...  
Keyword(s):  
Neural Network ◽  
Social Media ◽  
Neural Network Model ◽  
User Profile ◽  
User Profiling ◽  
Technology Evaluation ◽  
Data Set ◽  
Value Prediction ◽  
User Interests ◽  
Evaluation Scores

With the popularity of social media, there has been an increasing interest in user profiling and its applications nowadays. This paper presents our system named UIR-SIST for User Profiling Technology Evaluation Campaign in SMP CUP 2017. UIR-SIST aims to complete three tasks, including keywords extraction from blogs, user interests labeling and user growth value prediction. To this end, we first extract keywords from a user's blog, including the blog itself, blogs on the same topic and other blogs published by the same user. Then a unified neural network model is constructed based on a convolutional neural network (CNN) for user interests tagging. Finally, we adopt a stacking model for predicting user growth value. We eventually receive the sixth place with evaluation scores of 0.563, 0.378 and 0.751 on the three tasks, respectively.

A pretreatment method of wastewater based on artificial intelligence and fuzzy neural network system

2020 ◽  
Vol 39 (2) ◽  
pp. 1711-1720
Author(s):  
He Chan ◽  
Yan Nai-He
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Network Model ◽  
Neural Network Model ◽  
Fuzzy Neural Network ◽  
Saline Wastewater ◽  
Pretreatment Method ◽  
Fuzzy Function ◽  
Dissolved Oxygen Control ◽  
Fuzzy Neural

A pretreatment method of industrial saline wastewater based on Artificial Intelligence based fuzzy neural network analysis was proposed to improve the pretreatment accuracy of industrial saline wastewater. This method uses a four-layer AI fuzzy neural network model and proposes a graded fuzzy neural network model for pretreatment method of industrial saline wastewater, it includes input layer, fuzzification layer, fuzzy logical layer and output layer, and designs the framework and calculation mode of the fuzzy function block and the neural network module. Finally, the dynamic simulation experiments of dissolved oxygen control in the fifth zone and nitrate nitrogen control in the second zone are carried out based on the simulation benchmark model (BSM1) platform. The experimental results show that this approach can effectively raise the adaptive control accuracy of the system compared with PID, feed forward neural network and conventional recurrent neural network.

New Wars and Their Visual Representation: Dead Bodies without Graves/Mourne

2020 ◽  
Vol 7 (1) ◽  
pp. 57-76
Author(s):  
Tuncay Şur ◽  
Betül Yarar
Keyword(s):  
Social Media ◽  
Visual Representation ◽  
Human Beings ◽  
The Public ◽  
Photographic Images ◽  
War Photography ◽  
New Wars ◽  
The Social ◽  
History Of ◽  
The Impact

This paper seeks to understand why there has been an increase in photographic images exposing military violence or displaying bodies killed by military forces and how they can freely circulate in the public without being censored or kept hidden. In other words, it aims to analyze this particular issue as a symptom of the emergence of new wars and a new regime of their visual representation. Within this framework, it attempts to relate two kinds of literature that are namely the history of war and war photography with the bridge of theoretical discussions on the real, its photographic representation, power, and violence.  Rather than systematic empirical analysis, the paper is based on a theoretical attempt which is reflected on some socio-political observations in the Middle East where there has been ongoing wars or new wars. The core discussion of the paper is supported by a brief analysis of some illustrative photographic images that are served through the social media under the circumstances of war for instance in Turkey between Turkish military troops and the Kurdish militants. The paper concludes that in line with the process of dissolution/transformation of the old nation-state formations and globalization, the mechanism and mode of power have also transformed to the extent that it resulted in the emergence of new wars. This is one dynamic that we need to recognize in relation to the above-mentioned question, the other is the impact of social media in not only delivering but also receiving war photographies. Today these changes have led the emergence of new machinery of power in which the old modern visual/photographic techniques of representing wars without human beings, torture, and violence through censorship began to be employed alongside medieval power techniques of a visual exhibition of tortures and violence.

An Ensemble Neural Network Model for Benefiting Pregnancy Health Stats from Mining Social Media

2018 ◽  
pp. 3-15 ◽  
Author(s):  
Neha Warikoo ◽  
Yung-Chun Chang ◽  
Hong-Jie Dai ◽  
Wen-Lian Hsu
Keyword(s):  
Neural Network ◽  
Social Media ◽  
Network Model ◽  
Neural Network Model ◽  
Ensemble Neural Network
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