scholarly journals Multilayered MapReduce Framework to Link Cybernetic Vulnerabilities and Cybernetic Laws from E-News Articles

2021 ◽  
Author(s):  
SUDHANDRADEVI P
Keyword(s):  
Social Media ◽  
Complex Structure ◽  
Initial Step ◽  
Streaming Data ◽  
Public Access ◽  
Highly Qualified ◽  
Mapreduce Framework ◽  
Metropolitan Cities ◽  
Web Space ◽  
Percent Accuracy

Abstract The growth of technology evaluation and the influence of smart gazettes, which have a very complex structure, the amount of data in an organization, E-Commerce, and ERP explodes. When data is processed as described, it becomes the engine of every individual. According to projections from 2025, social media, IoT, streaming data, and geodata will generate 80% of unstructured data, and there will be 4.8 billion tech enthusiasts. The most popular social media trend allows users to access publicly available data. Hackers are highly qualified in both the web space and the dark web, and the rise of complexity and digitization of this public access will cause loopholes in legislation. The major goal of this study is to gather information about the cyber vulnerability of electronic news. Data collection, text standardization, and feature extraction were all part of the initial step. In the second step, MapReduce was used to obtain demographic insights using a multi-layered categorization strategy. Cybercrime is classified using a classifier technique, and the model has a 53 percent accuracy rate. Phishing is a result of cyber weaknesses, and it has been discovered in a higher number of metropolitan cities. Men, rather than women, make up the majority of crime victims. Individuals should be made aware of secure access to websites and media, according to the findings of the study. People should be aware of cyber vulnerabilities, as well as cyber laws enacted under the IPC, the IT Act 2000, and CERT-In.

scholarly journals Proposal of a composite indicator for measuring social media presence in the wine market

2020 ◽  
Author(s):  
Andrea Conchado Peiró ◽  
José Miguel Carot Sierra ◽  
Elena Vázquez Barrachina ◽  
Enrique Orduña Malea
Keyword(s):  
Social Media ◽  
Market Research ◽  
Initial Step ◽  
Composite Indicators ◽  
New Approach ◽  
Misleading Information ◽  
Social Media Networks ◽  
Wine Market ◽  
Definition Of ◽  
Research Analysis

Cybermetrics field is attracting considerable interest due to its utility as a data-oriented technique for research, though it may provide misleading information when used in complex systems. This paper outlines a new approach to market research analysis through the definition of composite indicators for cybermetrics, applied to the Spanish wine market. Our findings show that the majority of cellars were present in only one or two social media networks: Facebook, Twitter or both. Besides, the presence on the Web can be summarized into three principal components: website quality, presence on Facebook, and presence on Twitter. Three groups of cellars were identified according to their position in these components: cellars with a high number of errors in their website with complete absence of information in social media, cellars with strong presence in social media, and cellars in an intermediate position. Our results constitute an excellent initial step towards the definition of a methodology for building composite indicators in cybermetrics. From a practical approach, these indicators may encourage cellar managers to make better decisions towards their transition to the digital market.

scholarly journals Dynamic Language Models for Streaming Text

2014 ◽  
Vol 2 ◽  
pp. 181-192 ◽  
Author(s):  
Dani Yogatama ◽  
Chong Wang ◽  
Bryan R. Routledge ◽  
Noah A. Smith ◽  
Eric P. Xing
Keyword(s):  
Social Media ◽  
Temporal Dynamics ◽  
Language Model ◽  
Language Modeling ◽  
Streaming Data ◽  
Language Models ◽  
Linguistic Context ◽  
Text Data ◽  
Competing Models ◽  
Context Features

We present a probabilistic language model that captures temporal dynamics and conditions on arbitrary non-linguistic context features. These context features serve as important indicators of language changes that are otherwise difficult to capture using text data by itself. We learn our model in an efficient online fashion that is scalable for large, streaming data. With five streaming datasets from two different genres—economics news articles and social media—we evaluate our model on the task of sequential language modeling. Our model consistently outperforms competing models.

Real-Time Streaming Data Analysis Using a Three-Way Classification Method for Sentimental Analysis

2018 ◽  
Vol 13 (3) ◽  
pp. 99-111 ◽  
Author(s):  
Srinidhi Hiriyannaiah ◽  
G.M. Siddesh ◽  
K.G. Srinivasa
Keyword(s):  
Social Media ◽  
Opinion Mining ◽  
Streaming Data ◽  
Classification Method ◽  
Emotion Classification ◽  
Knowledge Based ◽  
Twitter Data ◽  
Internet Users ◽  
Word Classification ◽  
Three Stages

This article describes how recent advances in computing have led to an increase in the generation of data in fields such as social media, medical, power and others. With the rapid increase in internet users, social media has given power for sentiment analysis or opinion mining. It is a highly challenging task for storing, querying and analyzing such types of data. This article aims at providing a solution to store, query and analyze streaming data using Apache Kafka as the platform and twitter data as an example for analysis. A three-way classification method is proposed for sentimental analysis of twitter data that combines both the approaches for knowledge-based and machine-learning using three stages namely emotion classification, word classification and sentiment classification. The hybrid three-way classification approach was evaluated using a sample of five query strings on twitter and compared with existing emotion classifier, polarity classifier and Naïve Bayes classifier for sentimental analysis. The accuracy of the results of the proposed approach is superior when compared to existing approaches.

scholarly journals Regional Influenza Prediction with Sampling Twitter Data and PDE Model

2020 ◽  
Vol 17 (3) ◽  
pp. 678 ◽  
Author(s):  
Yufang Wang ◽  
Kuai Xu ◽  
Yun Kang ◽  
Haiyan Wang ◽  
Feng Wang ◽  
...  
Keyword(s):  
Social Media ◽  
Real Time ◽  
Streaming Data ◽  
Data Sampling ◽  
Timely Manner ◽  
Pde Model ◽  
Time Prediction ◽  
Twitter Data ◽  
Sampling Process ◽  
Natural Choice

The large volume of geotagged Twitter streaming data on flu epidemics provides chances for researchers to explore, model, and predict the trends of flu cases in a timely manner. However, the explosive growth of data from social media makes data sampling a natural choice. In this paper, we develop a method for influenza prediction based on the real-time tweet data from social media, and this method ensures real-time prediction and is applicable to sampling data. Specifically, we first simulate the sampling process of flu tweets, and then develop a specific partial differential equation (PDE) model to characterize and predict the aggregated flu tweet volumes. Our PDE model incorporates the effects of flu spreading, flu recovery, and active human interventions for reducing flu. Our extensive simulation results show that this PDE model can almost eliminate the data reduction effects from the sampling process: It requires lesser historical data but achieves stronger prediction results with a relative accuracy of over 90% on the 1% sampling data. Even for the more aggressive data sampling ratios such as 0.1% and 0.01% sampling, our model is still able to achieve relative accuracies of 85% and 83%, respectively. These promising results highlight the ability of our mechanistic PDE model in predicting temporal–spatial patterns of flu trends even in the scenario of small sampling Twitter data.

scholarly journals Tracking Disaster Footprints with Social Streaming Data

2020 ◽  
Vol 34 (01) ◽  
pp. 370-377
Author(s):  
Lu Cheng ◽  
Jundong Li ◽  
K. Selcuk Candan ◽  
Huan Liu
Keyword(s):  
Social Media ◽  
Nonnegative Matrix ◽  
Reconstruction Error ◽  
Streaming Data ◽  
Accurate Information ◽  
Topic Identification ◽  
Emergency Managers ◽  
Naive Method ◽  
Real World Datasets ◽  
Over Time

Social media has become an indispensable tool in the face of natural disasters due to its broad appeal and ability to quickly disseminate information. For instance, Twitter is an important source for disaster responders to search for (1) topics that have been identified as being of particular interest over time, i.e., common topics such as “disaster rescue”; (2) new emerging themes of disaster-related discussions that are fast gathering in social media streams (Saha and Sindhwani 2012), i.e., distinct topics such as “the latest tsunami destruction”. To understand the status quo and allocate limited resources to most urgent areas, emergency managers need to quickly sift through relevant topics generated over time and investigate their commonness and distinctiveness. A major obstacle to the effective usage of social media, however, is its massive amount of noisy and undesired data. Hence, a naive method, such as set intersection/difference to find common/distinct topics, is often not practical. To address this challenge, this paper studies a new topic tracking problem that seeks to effectively identify the common and distinct topics with social streaming data. The problem is important as it presents a promising new way to efficiently search for accurate information during emergency response. This is achieved by an online Nonnegative Matrix Factorization (NMF) scheme that conducts a faster update of latent factors, and a joint NMF technique that seeks the balance between the reconstruction error of topic identification and the losses induced by discovering common and distinct topics. Extensive experimental results on real-world datasets collected during Hurricane Harvey and Florence reveal the effectiveness of our framework.

mLearning and Creative Practices

2016 ◽  
Vol 8 (4) ◽  
pp. 34-43
Author(s):  
Laurent Antonczak ◽  
Helen Keegan ◽  
Thomas Cochrane
Keyword(s):  
Social Media ◽  
Public Access ◽  
Ethical Challenges ◽  
Creative People ◽  
Mobile Media ◽  
Learning And Teaching ◽  
Gender Minorities ◽  
Practice Based Research ◽  
Academic Sector

The ethos of open sharing of experiences and user generated content enabled by Mobile social media can be problematic in some cases (politics, gender, minorities), and it is not fully understood within the creative and academic sector. Creative people, students, and lecturers can misconceive the value and issues around open and public access to their work online, which include: professionalism, Intellectual Property (IP), collaboration (Gayeski, 2002; Londsdale, Baber, Sharples, & Arvanitis, 2003), peer esteem VS individualism, amateurism, and paranoia. Collectively the authors of this paper have accrued a wide portfolio of experiences in global educational collaboration and practice-based research and, in this position paper, they highlight some of the key ethical challenges that they have found need to be negotiated within global mobile social media education (Andrews, Dyson, Smyth, & Wallace, 2011) and mobile media production (i.e.: photography and video – Wishart & Green, 2010). In order to ground this reflective discussion, the authors use Heutagogy as the learning and teaching framework to guide the qualitative analysis of a specific case study which is built upon the scenario-based approach utilised by Andrews et al., (2013).

scholarly journals Harnessing Social Media to Explore Youth Social Withdrawal in Three Major Cities in China: Cross-Sectional Web Survey

2018 ◽  
Vol 5 (2) ◽  
pp. e34 ◽  
Author(s):  
Lucia Lin Liu ◽  
Tim MH Li ◽  
Alan R Teo ◽  
Takahiro A Kato ◽  
Paul WC Wong
Keyword(s):  
Social Media ◽  
Social Networking ◽  
Social Withdrawal ◽  
Web Survey ◽  
Cross Sectional ◽  
Metropolitan Cities ◽  
Social Media Platforms ◽  
Employment Education ◽  
Use Of The Internet ◽  
The Cost

Background Socially withdrawn youth belong to an emerging subgroup of youth who are not in employment, education, or training and who have limited social interaction intention and opportunities. The use of the internet and social media is expected to be an alternative and feasible way to reach this group of young people because of their reclusive nature. Objective The aim of this study was to explore the possibility of using various social media platforms to investigate the existence of the phenomenon of youth social withdrawal in 3 major cities in China. Methods A cross-sectional open Web survey was conducted from October 2015 to May 2016 to identify and reach socially withdrawn youth in 3 metropolitan cities in China: Beijing, Shanghai, and Shenzhen. To advertise the survey, 3 social media platforms were used: Weibo, WeChat, and Wandianba, a social networking gaming website. Results In total, 137 participants completed the survey, among whom 13 (9.5%) were identified as belonging to the withdrawal group, 7 (5.1%) to the asocial group, and 9 (6.6%) to the hikikomori group (both withdrawn and asocial for more than 3 months). The cost of recruitment via Weibo was US $7.27 per participant. Conclusions Several social media platforms in China are viable and inexpensive tools to reach socially withdrawn youth, and internet platforms that specialize in a certain culture or type of entertainment appeared to be more effective in reaching socially withdrawn youth.

scholarly journals An organisational cultivation of digital resignation?: Enterprise social media, privacy, and autonomy

2021 ◽  
Vol 42 (s4) ◽  
pp. 185-198
Author(s):  
Christoffer Bagger
Keyword(s):  
Social Media ◽  
Media Studies ◽  
Knowledge Workers ◽  
Research Question ◽  
Initial Step ◽  
Qualitative Interview ◽  
Enterprise Social Media ◽  
Qualitative Interview Study ◽  
Social Media Platforms ◽  
Research Gap

Abstract Enterprise social media (ESM) have largely gone ignored in discussions of the datafication practices of social media platforms. This article presents an initial step towards filling this research gap. My research question in this article regards how employees of companies using the ESM Workplace from Facebook feel that the implementation of this particular platform relates to their potential struggles for digital privacy and work–life segmentation. Methodologically, I explore this through a qualitative interview study of 21 Danish knowledge workers in different organisations using the ESM. The central analytical proposal of the article is that the interviewees express a “digital resignation” towards the implementation of the ESM. In contrast to previous discussions, this resignation cannot only be thought of as “corporately cultivated” by third parties, but must also be considered as “organisationally cultivated” by the organisations people work for. The study suggests that datafication-oriented media studies should consider organisational contexts.

scholarly journals Access to Government Officials in the Age of Social Media

2019 ◽  
Vol 1 (1) ◽  
pp. 30-58
Author(s):  
Katie Blevins ◽  
Kearston L. Wesner
Keyword(s):  
Social Media ◽  
Public Access ◽  
Concomitant Increase ◽  
Public Officials ◽  
Government Officials ◽  
Donald Trump ◽  
The Supreme Court ◽  
Social Media Platforms ◽  
Twitter Account ◽  
Speech Clause

As social media platforms have become more pervasive, there has been a concomitant increase in the number of government officials using their personal social media accounts to perform official government duties. Most notably, President Donald Trump continues to use his personal Twitter account, established in 2009, prior to his presidency, to conduct a variety of official tasks. While the First Amendment’s Free Speech Clause traditionally protects an individual’s right to engage in self-expression, the Supreme Court has not unequivocally recognized an affirmative right to know as an extension of the First Amendment. Recent court decisions suggest this may change. This study addresses the contours of public access to government officials on social media. Specifically, it considers the circumstances in which government officials are likely to be held to a standard of accountability and the case for treating public officials’ social media accounts as public forums, including how factors relating to account ownership and content impact that analysis.

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