Dark Patterns: Social Media, Gaming, and E-Commerce

2021 ◽  
Vol 65 (1) ◽  
pp. 752-756
Author(s):  
Ilayda Karagoel ◽  
Dan Nathan-Roberts
Keyword(s):  
Social Media ◽  
Human Behavior ◽  
Technical System ◽  
Marginal Benefit ◽  
Design Practices

Dark Patterns are defined as “tricks used in websites and apps that make you do things that you didn’t mean to” (Bringull, 2017). They are implemented to manipulate users with deceptive design tactics using studies on human behavior, and are coined as “anti-user”, since the marginal benefit of corporations are being prioritized over users. This proceeding specifically studies the prevalent dark patterns in the fields of Social Media, Gaming, and E-Commerce platforms. Though Grey et al. initially characterized dark patterns into 12 types of dark patterns (Gray et al., 2018), there are plenty of studies where more categorizations of dark patterns are found in different fields. Finally, this paper sheds light on what could be the next steps for the stakeholders, such as designers, engineers and the overall socio technical system, to better regulate dark patterns in order to minimize user concerns, as well as reduce unethical design practices.

SELFIE TOURISM DAN ELECTRONIC WORD OF MOUTH SEBAGAI ANTESEDEN RE-VISIT INTENTION

2019 ◽  
Vol 4 (3) ◽  
pp. 526
Author(s):  
Okki Trinanda ◽  
Astri Yuza Sari
Keyword(s):  
Social Media ◽  
Hypothesis Testing ◽  
Sample Size ◽  
Human Behavior ◽  
Word Of Mouth ◽  
Electronic Word Of Mouth ◽  
Tourist Attractions ◽  
Tourism Management ◽  
West Sumatra ◽  
First Time

<p><em>Research linking selfie behavior and tourism management is very rarely implemented. Selfie behavior is more researched as part of psychology that studies human behavior. This study aims to find out (1) the influence of Selfie Tourism on Electronic Word of Mouth, (2) the influence of Selfie Tourism on Re-Visit Intention, and (3) the influence of Electronic Word of Mouth on Re-Visit Intention. This study uses estimates based on the number of parameters obtained by the sample size of 452 respondents with accidental sampling. Respondents who were included in this study were foreign tourists and domestic tourists who visited the tourism sites in West Sumatra for the first time. While hypothesis testing uses SEM. In this study all relationships between variables were found to be positive and significant. The implication of this study is that tourism managers not only pay attention to aspects of service such as hospitality, cleanliness and so on, but also provide attractive tourist attractions to be photographed and distributed to social media.</em></p><p><em><br /></em></p><p><em>Penelitian yang menghubungkan perilaku selfie dan manajemen pariwisata sangat jarang dilaksanakan. Perilaku selfie lebih banyak diteliti sebagai bagian dari psikologi yang mempelajari perilaku manusia. Penelitian ini bertujuan untuk mengetahui (1) pengaruh Selfie Tourism terhadap Electronic Word of Mouth, (2) pengaruh Selfie Tourism terhadap Re-Visit Intention, dan (3) pengaruh Electronic Word of Mouth pada Re-Visit Intention. Penelitian ini menggunakan jumlah parameter yang diperoleh dengan ukuran sampel 452 responden dengan accidental sampling. Responden yang dikunjungi oleh wisatawan asing dan wisatawan domestik yang mengunjungi situs pariwisata di Sumatera Barat untuk pertama kalinya. Sedangkan pengujian hipotesis menggunakan SEM. Dalam penelitian ini semua hubungan antar variabel ditemukan positif dan signifikan. Implikasi dari penelitian ini adalah bahwa manajer pariwisata tidak hanya memperhatikan layanan dan kebersihan tetapi juga menyediakan media sosial.</em></p>

scholarly journals A computational reward learning account of social media engagement

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Björn Lindström ◽  
Martin Bellander ◽  
David T. Schultner ◽  
Allen Chang ◽  
Philippe N. Tobler ◽  
...  
Keyword(s):  
Social Media ◽  
Human Behavior ◽  
Computational Models ◽  
Average Rate ◽  
Human Life ◽  
Modern Human ◽  
Reward Learning ◽  
Learning Mechanisms ◽  
Social Media Engagement ◽  
Social Rewards

AbstractSocial media has become a modern arena for human life, with billions of daily users worldwide. The intense popularity of social media is often attributed to a psychological need for social rewards (likes), portraying the online world as a Skinner Box for the modern human. Yet despite such portrayals, empirical evidence for social media engagement as reward-based behavior remains scant. Here, we apply a computational approach to directly test whether reward learning mechanisms contribute to social media behavior. We analyze over one million posts from over 4000 individuals on multiple social media platforms, using computational models based on reinforcement learning theory. Our results consistently show that human behavior on social media conforms qualitatively and quantitatively to the principles of reward learning. Specifically, social media users spaced their posts to maximize the average rate of accrued social rewards, in a manner subject to both the effort cost of posting and the opportunity cost of inaction. Results further reveal meaningful individual difference profiles in social reward learning on social media. Finally, an online experiment (n = 176), mimicking key aspects of social media, verifies that social rewards causally influence behavior as posited by our computational account. Together, these findings support a reward learning account of social media engagement and offer new insights into this emergent mode of modern human behavior.

Virtual Interactions via Smartphones

2012 ◽  
pp. 189-198
Author(s):  
Jeongyoon Lee ◽  
R. Karl Rethemeyer
Keyword(s):  
Mobile Phone ◽  
Mobile Devices ◽  
Human Behavior ◽  
Mobile Technology ◽  
Technical System ◽  
Key Dimensions ◽  
Virtual Interactions

The recent boom in the use of smartphones has led to an expansion of the concept of cyber behavior to include nearly perpetual virtual contact through mobile devices. This chapter addresses the issue of mobile cyber behavior by identifying key dimensions of virtual interactions through smartphones. While most prior studies focused on mobile technology from a technical perspective, this article takes a sociotechnical perspective, focusing on aspects of human behavior in the context of a new technical system (i.e., smartphones). The authors’ review of this literature suggests that mobile phone cyber behavior develops along three primary dimensions – the “3Cs” of: contextualization, customization, and convenience.

scholarly journals Human Behavior Analysis Using Intelligent Big Data Analytics

2021 ◽  
Vol 12 ◽  
Author(s):  
Muhammad Usman Tariq ◽  
Muhammad Babar ◽  
Marc Poulin ◽  
Akmal Saeed Khattak ◽  
Mohammad Dahman Alshehri ◽  
...  
Keyword(s):  
Social Media ◽  
Big Data ◽  
Human Behavior ◽  
Data Analytics ◽  
Data Science ◽  
Big Data Analytics ◽  
Apache Spark ◽  
Social Media Data ◽  
The Social ◽  
Media Data

Intelligent big data analysis is an evolving pattern in the age of big data science and artificial intelligence (AI). Analysis of organized data has been very successful, but analyzing human behavior using social media data becomes challenging. The social media data comprises a vast and unstructured format of data sources that can include likes, comments, tweets, shares, and views. Data analytics of social media data became a challenging task for companies, such as Dailymotion, that have billions of daily users and vast numbers of comments, likes, and views. Social media data is created in a significant amount and at a tremendous pace. There is a very high volume to store, sort, process, and carefully study the data for making possible decisions. This article proposes an architecture using a big data analytics mechanism to efficiently and logically process the huge social media datasets. The proposed architecture is composed of three layers. The main objective of the project is to demonstrate Apache Spark parallel processing and distributed framework technologies with other storage and processing mechanisms. The social media data generated from Dailymotion is used in this article to demonstrate the benefits of this architecture. The project utilized the application programming interface (API) of Dailymotion, allowing it to incorporate functions suitable to fetch and view information. The API key is generated to fetch information of public channel data in the form of text files. Hive storage machinist is utilized with Apache Spark for efficient data processing. The effectiveness of the proposed architecture is also highlighted.

scholarly journals Psychological Analysis of Jokowi’s First 100 Days and Nawacita from Text in Twitter

2018 ◽  
Vol 21 (3) ◽  
pp. 173
Author(s):  
Indro Adinugroho ◽  
Smitha Sjahputri ◽  
Judotens Budiarto ◽  
Roby Muhamad
Keyword(s):  
Social Media ◽  
Public Opinion ◽  
Human Behavior ◽  
Strategic Plan ◽  
Political Issues ◽  
Public Response ◽  
The Public ◽  
Psychological Analysis ◽  
Valence And Arousal ◽  
Negative Sentiment

In recent days, the public often uses social media such as Twitter for delivering critics; appreciation and campaign related to Government and political issues. The existence of Twitter is changing human behavior rapidly. This study aims to identify Twitter as a medium to generate public opinion concerning two political issues, the 7th Indonesian President first 100 days and public response towards his strategic plan, Nawacita. Method applied in this study is a combination of contemporary research instruments that combines technology and psychology. In this study, the authors examined conversation on Twitter by using Tracker and Algoritma Kata (AK, words algorithm). Tracker is used to collecting conversation on twitter regarding Jokowi’s first 100 days and Nawacita, whereas AK is applied to identify valence and arousal in each tweet collected by Tracker. The finding shows the domination of positive tweets in every week. However, there is a moment where the number of positive tweets was close to negative tweets. In Nawacita issue, law reformation and enforcement was the issue that has highest negative sentiment among others.

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.

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