scholarly journals Towards a Digital Sensorial Archaeology as an Experiment in Distant Viewing of the Trade in Human Remains on Instagram

Heritage ◽  
2020 ◽  
Vol 3 (2) ◽  
pp. 208-227 ◽  
Author(s):  
Shawn Graham ◽  
Damien Huffer ◽  
Jeff Blackadar
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Social Media ◽  
Cloud Computing ◽  
Human Remains ◽  
The Self ◽  
Self Presentation ◽  
Social Media Platform ◽  
Automated Annotation ◽  
Media Platform

It is possible to purchase human remains via Instagram. We present an experiment using computer vision and automated annotation of over ten thousand photographs from Instagram, connected with the buying and selling of human remains, in order to develop a distant view of the sensory affect of these photos: What macroscopic patterns exist, and how do these relate to the self-presentation of these individual vendors? Using Microsoft’s Azure cloud computing and machine learning services, we annotate and then visualize the co-occurrence of tags as a series of networks, giving us that macroscopic view. Vendors are clearly trying to mimic ‘museum’-like experiences, with differing degrees of effectiveness. This approach may therefore be useful for even larger-scale investigations of this trade beyond this single social media platform.

scholarly journals Linguistic drivers of misinformation diffusion on social media during the COVID-19 pandemic

2021 ◽  
Author(s):  
Giandomenico Di Domenico ◽  
Annamaria Tuan ◽  
Marco Visentin
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
New Technologies ◽  
Fake News ◽  
Conspiracy Theories ◽  
Social Media Platform ◽  
The Social ◽  
Media Platform ◽  
Textual Cues ◽  
Twitter Users

AbstractIn the wake of the COVID-19 pandemic, unprecedent amounts of fake news and hoax spread on social media. In particular, conspiracy theories argued on the effect of specific new technologies like 5G and misinformation tarnished the reputation of brands like Huawei. Language plays a crucial role in understanding the motivational determinants of social media users in sharing misinformation, as people extract meaning from information based on their discursive resources and their skillset. In this paper, we analyze textual and non-textual cues from a panel of 4923 tweets containing the hashtags #5G and #Huawei during the first week of May 2020, when several countries were still adopting lockdown measures, to determine whether or not a tweet is retweeted and, if so, how much it is retweeted. Overall, through traditional logistic regression and machine learning, we found different effects of the textual and non-textual cues on the retweeting of a tweet and on its ability to accumulate retweets. In particular, the presence of misinformation plays an interesting role in spreading the tweet on the network. More importantly, the relative influence of the cues suggests that Twitter users actually read a tweet but not necessarily they understand or critically evaluate it before deciding to share it on the 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

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 Analisis Sentimen Pada Maskapai Penerbangan di Platform Twitter Menggunakan Algoritma Support Vector Machine (SVM)

Teknika ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 18-26
Author(s):  
Hendry Cipta Husada ◽  
Adi Suryaputra Paramita
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Support Vector Machine ◽  
Cross Validation ◽  
Support Vector ◽  
Learning Approach ◽  
Social Media Platform ◽  
Machine Learning Approach ◽  
Media Platform ◽  
Fold Cross Validation

Perkembangan teknologi saat ini telah memberikan kemudahan bagi banyak orang dalam mendapatkan dan menyebarkan informasi di berbagai social media platform. Twitter merupakan salah satu media yang kerap digunakan untuk menyampaikan opini sebagai bentuk reaksi seseorang atas suatu hal. Opini yang terdapat di Twitter dapat digunakan perusahaan maskapai penerbangan sebagai parameter kunci untuk mengetahui tingkat kepuasan publik sekaligus bahan evaluasi bagi perusahaan. Berdasarkan hal tersebut, diperlukan sebuah metode yang dapat secara otomatis melakukan klasifikasi opini ke dalam kategori positif, negatif, atau netral melalui proses analisis sentimen. Proses analisis sentimen dilakukan dengan proses data preprocessing, pembobotan kata menggunakan metode TF-IDF, penerapan algoritma, dan pembahasan atas hasil klasifikasi. Klasifikasi opini dilakukan dengan machine learning approach memanfaatkan algoritma multi-class Support Vector Machine (SVM). Data yang digunakan dalam penelitian ini adalah opini dalam bahasa Inggris dari para pengguna Twitter terhadap maskapai penerbangan. Berdasarkan pengujian yang telah dilakukan, hasil klasifikasi terbaik diperoleh menggunakan SVM kernel RBF pada nilai parameter 𝐶(complexity) = 10 dan 𝛾(gamma) = 1, dengan nilai accuracy sebesar 84,37% dan 80,41% ketika menggunakan 10-fold cross validation.

scholarly journals Identification of Online Public Shaming Using Machine Learning Framework

2021 ◽  
pp. 539-543
Author(s):  
Sonali Gaikwad ◽  
Tejashri Borate ◽  
Nandpriya Ashtekar ◽  
Umadevi Lade
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Negative Impact ◽  
Daily Life ◽  
Irrelevant Information ◽  
Social Media Platform ◽  
Learning Framework ◽  
Huge Impact ◽  
Social Media Platforms ◽  
Media Platform

Social Media Platforms involve not millions but billions of users around the globe. Interactions on these easily available social media sites like Twitter have a huge impact on people. Nowadays, there is undesirable negative impact for daily life. These hugely used major platforms of communication have now become a great source of dispersing unwanted data and irrelevant information, Twitter being one of the most extravagant social media platform in our times, the topmost popular microblogging services is now used as a weapon to share unethical, unreasonable amount of opinions, media. In this proposed work the dishonouring comments, tweets towards people are categorized into 9 types. The tweets are further classifies into one of these types or non-shaming tweets towards people. Observation says out of the multitude of taking an interested clients who posts remarks on a specific occasion, lions share are probably going to modify the person in question. Moreover, it is not the nonshaming devotee who checks the increment quicker but of shaming in twitter.

scholarly journals Social Media as a Research Tool (SMaaRT) for Risky Behavior Analytics: Methodological Review

10.2196/21660 ◽  
2020 ◽  
Vol 6 (4) ◽  
pp. e21660
Author(s):  
Tavleen Singh ◽  
Kirk Roberts ◽  
Trevor Cohen ◽  
Nathan Cobb ◽  
Jing Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Health Behavior ◽  
Health Behaviors ◽  
Peer Interactions ◽  
Social Media Platform ◽  
Risky Health Behaviors ◽  
Textual Data ◽  
Social Media Platforms ◽  
Media Platform

Background Modifiable risky health behaviors, such as tobacco use, excessive alcohol use, being overweight, lack of physical activity, and unhealthy eating habits, are some of the major factors for developing chronic health conditions. Social media platforms have become indispensable means of communication in the digital era. They provide an opportunity for individuals to express themselves, as well as share their health-related concerns with peers and health care providers, with respect to risky behaviors. Such peer interactions can be utilized as valuable data sources to better understand inter-and intrapersonal psychosocial mediators and the mechanisms of social influence that drive behavior change. Objective The objective of this review is to summarize computational and quantitative techniques facilitating the analysis of data generated through peer interactions pertaining to risky health behaviors on social media platforms. Methods We performed a systematic review of the literature in September 2020 by searching three databases—PubMed, Web of Science, and Scopus—using relevant keywords, such as “social media,” “online health communities,” “machine learning,” “data mining,” etc. The reporting of the studies was directed by the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Two reviewers independently assessed the eligibility of studies based on the inclusion and exclusion criteria. We extracted the required information from the selected studies. Results The initial search returned a total of 1554 studies, and after careful analysis of titles, abstracts, and full texts, a total of 64 studies were included in this review. We extracted the following key characteristics from all of the studies: social media platform used for conducting the study, risky health behavior studied, the number of posts analyzed, study focus, key methodological functions and tools used for data analysis, evaluation metrics used, and summary of the key findings. The most commonly used social media platform was Twitter, followed by Facebook, QuitNet, and Reddit. The most commonly studied risky health behavior was nicotine use, followed by drug or substance abuse and alcohol use. Various supervised and unsupervised machine learning approaches were used for analyzing textual data generated from online peer interactions. Few studies utilized deep learning methods for analyzing textual data as well as image or video data. Social network analysis was also performed, as reported in some studies. Conclusions Our review consolidates the methodological underpinnings for analyzing risky health behaviors and has enhanced our understanding of how social media can be leveraged for nuanced behavioral modeling and representation. The knowledge gained from our review can serve as a foundational component for the development of persuasive health communication and effective behavior modification technologies aimed at the individual and population levels.

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 Survey on Identification of Online Public Shaming Using Machine Learning Framework

2021 ◽  
pp. 430-433
Author(s):  
Prof. Priti Jorvekar ◽  
Sonali Gaikwad ◽  
Nandpriya Ashtekar ◽  
Tejashri Borate ◽  
Umadevi Lade
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Negative Impact ◽  
Daily Life ◽  
Irrelevant Information ◽  
Social Media Platform ◽  
Learning Framework ◽  
Huge Impact ◽  
Social Media Platforms ◽  
Media Platform

Social Media Platforms involve not millions but billions of users around the globe. Interactions on these easily available social media sites like Twitter have a huge impact on people. Nowadays, there is undesirable negative impact for daily life. These hugely used major platforms of communication have now become a great source of dispersing unwanted data and irrelevant information, Twitter being one of the most extravagant social media platform in our times, the topmost popular microblogging services is now used as a weapon to share unethical, unreasonable amount of opinions, media. In this proposed work the dishonouring comments, tweets towards people are categorized into 9 types. The tweets are further classifies into one of these types or non-shaming tweets towards people. Observation says out of the multitude of taking an interested clients who posts remarks on a specific occasion, lions share are probably going to modify the person in question. Moreover, it is not the nonshaming devotee who checks the increment quicker but of shaming in twitter.

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