scholarly journals Gender Estimation on Social Media Using Recurrent Neural Network

2021 ◽  
Vol 10 (3) ◽  
pp. 1802-1812
Keyword(s):  
Social Media ◽  
Instant Messaging ◽  
Focal Point ◽  
Text Messages ◽  
Unstructured Data ◽  
Gender Orientation ◽  
Grouping Strategies ◽  
Application Programming ◽  
The Individual ◽  
Programming Interfaces

With the development of instant messaging innovation and social media, protection has turned into a significant issue. There is a danger of one’s record being hacked and utilized by the unknown person unconsciously. While doing texting on social media many people use abbreviations, short messages, emojis, images. We tried with different methods to gain the best accuracy in this research. In this paper, we will attempt to check the personality of the individual based on his/her composing style. We will explore the possibility of predicting the gender of a writer utilizing semantic proof. For this reason, term and style-based grouping strategies are assessed over an enormous accumulation of text messages. This study depicts the development of a huge, multilingual dataset named with gender, and examines factual models for deciding the gender of unknown Twitter clients. Twitter gives a basic method to clients to express sentiments, thoughts and assessments, makes the client produced content and related metadata, accessible to the network, and gives simple to utilize web and application programming interfaces to get to the information. The fundamental focal point of this paper is to gather the gender orientation of the client from unstructured data, including the username, screen name, depiction and picture, or by the client produced content

scholarly journals A Framework to process Text Data of Web Discussion Forums A Study of LisLinks

2019 ◽  
Vol 39 (06) ◽  
pp. 315-321
Author(s):  
Mohit Garg ◽  
Uma Kanjilal
Keyword(s):  
Social Media ◽  
Unstructured Data ◽  
The Internet ◽  
Discussion Forums ◽  
Text Data ◽  
Knowledge Repository ◽  
Social Media Platforms ◽  
Statistical Programming ◽  
Application Programming ◽  
Programming Interfaces

Nowadays, people use the internet for both seeking and disseminating information in a collaborative way on various social media platforms like Quora, Yahoo Answers, LisLinks Forum, etc. This social interaction on different topics makes these platforms as a knowledge repository. Evaluation of these repositories can help to understand various trends. However, this evaluation is a challenging task because of unstructured data and the unavailability of application programming interfaces for the harvesting of a dataset. This study presented a framework to harvest and pre-processing of data available on LisLinks Forum. The proposed framework is implemented using statistical programming language R. The fourteen metadata elements were defined for the discussion forums. The framework automatically harvest and pre-process relevant data of posts.

The evolution of rumors on a closed platform during COVID-19 (Preprint)

2021 ◽  
Author(s):  
Andrea Wen-Yi Wang ◽  
Jo-Yu Lan ◽  
Ming-Hung Wang ◽  
Chihhao Yu
Keyword(s):  
Social Media ◽  
World Health Organization ◽  
Psychological Health ◽  
Instant Messaging ◽  
Medical Personnel ◽  
Text Messages ◽  
World Health ◽  
The World ◽  
Physical And Psychological Health ◽  
Health Organization

BACKGROUND In 2020, the COVID-19 pandemic put the world in crisis on both physical and psychological health. Simultaneously, a myriad of unverified information flowed on social media and online outlets. The situation was so severe that the World Health Organization identified it an infodemic on February 2020. OBJECTIVE We want to study the propagation patterns and textual transformation of COVID-19 related rumors on a closed-platform. METHODS We obtained a dataset of 114 thousand suspicious text messages collected on Taiwan’s most popular instant messaging platform, LINE. We also proposed an algorithm that efficiently cluster text messages into groups, where each group contains text messages within limited difference in content. Each group then represents a rumor and elements in each group is a message about the rumor. RESULTS 114 thousand messages were separated into 937 groups with at least 10 elements. Of the 936 rumors, 44.5% (417) were related to COVID-19. By studying 3 popular false COVID-19 rumors, we identified that key authoritative figures, mostly medical personnel, were often quoted in the messages. Also, rumors resurfaced multiple times after being fact-checked, and the resurfacing pattern were influenced by major societal events and successful content alterations, such as changing whom to quote in a message. CONCLUSIONS To fight infodemic, it is crucial that we first understand why and how a rumor becomes popular. While social media gives rise to unprecedented number of unverified rumors, it also provides a unique opportunity for us to study rumor propagations and the interactions with society. Therefore, we must put more effort in the areas.

Web 2.0 in Governance

2016 ◽  
pp. 1674-1696
Author(s):  
Lefkothea Spiliotopoulou ◽  
Yannis Charalabidis
Keyword(s):  
Social Media ◽  
Web 2.0 ◽  
Opinion Mining ◽  
Policy Formulation ◽  
Web Based ◽  
Policy Makers ◽  
Web 3.0 ◽  
Significant Research ◽  
Application Programming ◽  
Programming Interfaces

There has been significant research in the private sector towards systematic exploitation of the emerging Web 2.0/Web 3.0 and social media paradigms. However, not much has been achieved with regards to the embodiment of similar technologies. Currently, governments and organizations are making considerable efforts, trying to enhance citizens' participation in decision-making and policy-formulation processes. This chapter presents a novel policy analysis framework, proposing a Web-based platform that enables publishing content and micro-applications to multiple Web 2.0 social media and collecting citizens' interactions (e.g. comments, ratings) with efficient use of Application Programming Interfaces (APIs) of these media. Citizens' opinions and interactions can then be processed through different techniques or methods (Web analytics, opinion mining, simulation modeling) in order to use the extracted conclusions as support to government decision and policy makers.

scholarly journals Mood State and Behavior Predictions in Social Media through Unstructured Data Analysis

2019 ◽  
pp. 1-9
Author(s):  
Gurpreet Singh Bawa ◽  
Suresh Kumar Sharma ◽  
Kanchan K. Jain
Keyword(s):  
Social Media ◽  
Data Analysis ◽  
Personality Trait ◽  
Mood State ◽  
Dirichlet Distribution ◽  
Probability Model ◽  
Unstructured Data ◽  
Latent Features ◽  
The Individual ◽  
And Behavior

For mood State and Behavior Predictions in Social Media through Unstructured Data Analysis, a new model, Behavior Dirichlet Probability Model (BDPM), which can capture the Behavior and Mood of user on Social media is proposed using Dirichlet distribution. There is a colossal amount of data being generated regularly on social media in the form of text from various channels by individuals in the form of posts, tweets, status, comments, blogs, reviews etc. Most of it belongs to some conversation where real-world individuals discuss, analyze, comment, exchange information. Deriving personality traits from textual data can be useful in observing the underlying attributes of the author’s personality which might explain a lot about their behavior, traits etc. These insights of the individual can be utilized to obtain a clear picture of their personality and accordingly a variety of services, utilities would follow automatically. Using Dirichlet probability distribution, the aim is to estimate the probability of each personality trait (or mood state) for an author and then model the latent features in the text which are not captured by the BDPM. As a result, the study can be helpful in prediction of mood state/personality trait as well as capturing the significance of the latent features apart from the ones present in the taxonomies, which will help in making an improved mood state or personality prediction.

The Construction of a New Sociality through Social Media: The Case of the Gezi Uprising in Turkey

2015 ◽  
Vol 2 (1) ◽  
pp. 72-99 ◽  
Author(s):  
Balca Arda
Keyword(s):  
Social Media ◽  
Social Relationships ◽  
Collective Identity ◽  
Mass Movement ◽  
Text Messages ◽  
Shopping Mall ◽  
Self In Relation ◽  
State Apparatus ◽  
The Individual ◽  
Diverse Groups

During Turkey’s Gezi Park Protests in the summer of 2013, millions of people became connected as fellow pro- testers. In the early days of the Gezi movement, the increase in participatory activism through social media made visible the police brutality exercised in the last days of May 2013 against a small group of environmentalists who were protecting Gezi Park from being demolished in order to build a shopping mall. Throughout Turkey’s political history, there has been no other example of this kind of spontaneous mass movement resisting the state apparatus with the large participation of diverse groups and self-convened protesters, without any dominant ideological appeal or leader affiliation. In this article, I will analyze the ways in which these patterns of contra- dictory interactions formed, evaluated, or triggered various types of social relationships, by critically examining the content of viral images, memes, and widely shared posts by Gezi protesters on social media. In the absence of internal cohesion or an ideological and organizational agenda, I argue that widely shared viral images, memes, and text messages provided the content to collaboratively construct and publicly frame the autonomous logic of the “Gezi spirit” by the Gezi protesters. I aim to analyze this new understanding of collective identity in autono- mous logic processed through social media as a being-with (mit-sein), rather than a fusion of the individual to an enigmatic we-ness in order to represent “I”. I claim that this autonomous collectivity is driven by fluidarity as a public experience of the self in relation to the other without intermediary apparatuses and hence can be conceptualized as having built a new sociality. 

scholarly journals Analisa Forensik Whatsapp Messanger Pada Smartphone Android

2020 ◽  
Vol 12 (1) ◽  
pp. 83
Author(s):  
Nenny Anggraini ◽  
Siti Ummi Masruroh ◽  
Hapsari Tiaraningtias
Keyword(s):  
Social Media ◽  
Instant Messaging ◽  
Simulation Method ◽  
Text Messages ◽  
Internet Technology ◽  
Cyber Crime ◽  
Forensic Evidence ◽  
Video File ◽  
Digital Forensic ◽  
Almost All

Abstract Internet technology and smartphones are increasingly rapidly followed by the rise of social media users, especially instant messaging that can be accessed using a smartphone, especially Android. One of the problems of social media is cyber crime that utilizes social media. Based on data from Instant Checkmate in 2014, 30,000 websites were hacked, and 12 casualties fell within a fraction of the crime from fraud to sex crimes, and it occurs in cyber crime involving social media, including instant media WhatsApp messenger. So it takes the forensic digital process to look for evidence of the crime, because basically there is no crime that does not leave a trace. This study was conducted to find the forensic evidence on the WhatsApp messenger application accessed on Android smartphones. WhatsApp messenger was chosen because it used to reach 1.5 billion users from over 2.7 billion users of social media worldwide. In this study, the simulation method used in the study to run 15 scenarios, including the return of the deleted files, the search for forensic evidence such as name and account number, a list of names and contact numbers, group chat, and text messages, pictures, video, and document files on personal chat, then text messages, pictures, videos, document files, voice notes, and location in group chat. The results of this study indicate that almost all forensic evidence traces in the WhatsApp messenger application are found, but the URL media can not be opened because it is encrypted by WhatsApp. Keyword: Digital Forensic, Forensic Evidence, Smartphone, WhatsApp Messenger.  Abstrak Perkembangan teknologi internet dan smartphone yang semakin pesat diikuti pula oleh meningkatnya pengguna media social pada instant messager yang diakses menggunakan smartphone khususnya Android. salah satu permasalahan yang tidak luput dari media sosial adalah tindak kejahatan dunia maya yang memanfaatkan media sosial. Berdasarkan data dari Instant Checkmate pada tahun 2014 sebanyak 30.000 website diretas, dan 12 korban perdetik berjatuhan dari berbagai aspek kejahatan dari penipuan hingga kejahatan seks, dan hal tersebut terjadi dalam praktek kejahatan internet (cyber crime) melibatkan media sosial, termasuk media instant messanger WhatsApp. Sehingga diperlukannya proses digital forensik untuk mencari bukti-bukti kejahatan tersebut, karena pada dasarnya tidak ada kejahatan yang tidak meninggalkan jejak. Penelitian ini dilakukan untuk menemukan bukti-bukti forensik tersebut pada aplikasi WhatsApp messanger yang diakses pada smartphone Android. WhatsApp messanger dipilih karena digunakan mencapai 1,5 tiliyun user dari lebih dari 2,7 triliyun pengguna media sosial seluruh dunia. Pada penelitian ini, metode simulasi digunakan dalam penelitian dengan menjalankan 15 skenario, diantaranya adalah pengembalian file yang dihapus, pencarian bukti forensik berupa nama dan nomor akun, daftar nama dan nomor kontak, group chat, kemudian pesan teks, gambar, video, dan file dokumen pada personal chat, kemudian pesan teks,  gambar, video, file dokumen, voice note, dan location pada group chat. Hasil dari penelitian ini menunjukkan bahwa hampir semua jejak bukti forensik pada aplikasi WhatsApp messanger berhasil ditemukan, namun media URL tidak dapat dibuka karena terenkripsi oleh WhatsApp.Keyword: Bukti Forensik, Digital Forensik, Smartphone, WhatsApp Messanger.

Visual Social Media and Big Data. Interpreting Instagram Images Posted on Twitter

2016 ◽  
Vol 2 (2) ◽  
pp. 113-134 ◽  
Author(s):  
Dhiraj Murthy ◽  
Alexander Gross ◽  
Marisa McGarry
Keyword(s):  
Social Media ◽  
Image Data ◽  
Hurricane Sandy ◽  
Social Media Data ◽  
Production And Consumption ◽  
The Social ◽  
Application Programming ◽  
Programming Interfaces ◽  
Media Data

Abstract Social media such as Twitter and Instagram are fast, free, and multicast. These attributes make them particularly useful for crisis communication. However, the speed and volume also make them challenging to study. Historically, journalists controlled what/how images represented crises. Large volumes of social media can change the politics of representing disasters. However, methodologically, it is challenging to study visual social media data. Specifically, the process is usually labour-intensive, using human coding of images to discern themes and subjects. For this reason, Studies investigating social media during crises tend to examine text. In addition, application programming interfaces (APIs) for visual social media services such as Instagram and Snapchat are restrictive or even non-existent. Our work uses images posted by Instagram users on Twitter during Hurricane Sandy as a case study. This particular case is unique as it is perhaps the first US disaster where Instagram played a key role in how victims experienced Sandy. It is also the last major US disaster to take place before Instagram images were removed from Twitter feeds. Our sample consists of 11,964 Instagram images embedded into tweets during a twoweek timeline surrounding Hurricane Sandy. We found that the production and consumption of selfies, food/drink, pets, and humorous macro images highlight possible changes in the politics of representing disasters - a potential turn from top-down understandings of disasters to bottom-up, citizen informed views. Ultimately, we argue that image data produced during crises has potential value in helping us understand the social experience of disasters, but studying these types of data presents theoretical and methodological challenges.

Web 2.0 in Governance

2014 ◽  
pp. 32-54
Author(s):  
Lefkothea Spiliotopoulou ◽  
Yannis Charalabidis
Keyword(s):  
Social Media ◽  
Web 2.0 ◽  
Opinion Mining ◽  
Policy Formulation ◽  
Web Based ◽  
Policy Makers ◽  
Web 3.0 ◽  
Significant Research ◽  
Application Programming ◽  
Programming Interfaces

There has been significant research in the private sector towards systematic exploitation of the emerging Web 2.0/Web 3.0 and social media paradigms. However, not much has been achieved with regards to the embodiment of similar technologies. Currently, governments and organizations are making considerable efforts, trying to enhance citizens’ participation in decision-making and policy-formulation processes. This chapter presents a novel policy analysis framework, proposing a Web-based platform that enables publishing content and micro-applications to multiple Web 2.0 social media and collecting citizens’ interactions (e.g. comments, ratings) with efficient use of Application Programming Interfaces (APIs) of these media. Citizens’ opinions and interactions can then be processed through different techniques or methods (Web analytics, opinion mining, simulation modeling) in order to use the extracted conclusions as support to government decision and policy makers.

The Janzen familicide

2017 ◽  
Author(s):  
Elizabeth Yardley
Keyword(s):  
Social Media ◽  
British Columbia ◽  
Focal Point ◽  
Mainstream Media ◽  
Structural Context ◽  
The Individual

This chapter analyses the Janzen familicide that took place on April 28, 2015 in British Columbia, Canada. The perpetrator of the crime was Randy Janzen, who made a confession in his Facebook page that he shot his nineteen-year-old daughter, Emily, in the head because she suffered from migraines. He also admitted to fatally shooting his wife, Laurel, and his sister, Shelly, that same day. Randy eventually committed suicide by shooting himself in the head. His Facebook confession appeared to be the focal point of the stories in international mainstream media and was the factor that first drew the author's attention to the case. The chapter first considers the individual, familial, local and structural context of the Janzen family before discussing the Janzens' social media lives and practices. It also compares Randy's use of networked media with that of Derek Medina.

scholarly journals Public Perception of Autonomous Mobility Using ML-Based Sentiment Analysis over Social Media Data

Logistics ◽  
2020 ◽  
Vol 4 (2) ◽  
pp. 12 ◽  
Author(s):  
Nikolaos Bakalos ◽  
Nikolaos Papadakis ◽  
Antonios Litke
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Public Perception ◽  
Analysis Tool ◽  
Social Media Platforms ◽  
Realistic View ◽  
Application Programming ◽  
Autonomous Mobility ◽  
Programming Interfaces ◽  
Media Data

The purpose of this article is to present a framework for capturing and analyzing social media posts using a sentiment analysis tool to determine the views of the general public towards autonomous mobility. The paper presents the systems used and the results of this analysis, which was performed on social media posts from Twitter and Reddit. To achieve this, a specialized lexicon of terms was used to query social media content from the dedicated application programming interfaces (APIs) that the aforementioned social media platforms provide. The captured posts were then analyzed using a sentiment analysis framework, developed using state-of-the-art deep machine learning (ML) models. This framework provides labeling for the captured posts based on their content (i.e., classifies them as positive or negative opinions). The results of this classification were used to identify fears and autonomous mobility aspects that affect negative opinions. This method can provide a more realistic view of the general public’s perception of automated mobility, as it has the ability to analyze thousands of opinions and encapsulate the users’ opinion in a semi-automated way.

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