Technologies and principles of unstructured distributed data processing in the context of modern media content providing

2020 ◽  
Vol 5 ◽  
pp. 22-28
Author(s):  
S.V. Kuleshov ◽  
◽  
A.A. Zaytseva ◽  
S.P. Levashkin ◽  
Keyword(s):  
Language Processing ◽  
Modern Society ◽  
Media Content ◽  
Distributed Data ◽  
Social Phenomena ◽  
Additional Advantage ◽  
Information Models ◽  
Ontological Approach ◽  
Complex Monitoring ◽  
Media Data

Purpose The COVID-19 pandemic is creating serious challenges for modern society that leads to develop new information models and methods of digital monitoring not only of the spread of the virus, but also of the socio-economic environment. Materials and methods: As sources for clarifying the parameters of such models, it is advisable to choose not a limited set of predefi ned Internet sources, but unstructured media data on an unlimited set of resources, which leads to the need to build a system for complex monitoring of social phenomena. Such system can supplement and correct mathematical and information models for the spread of viruses, aimed at minimizing the damage caused by any pandemic. Results: It is proposed to create a software system that includes a Data Retrieving subsystem (for collecting and preprocessing media data) combined with a headless browser. This allows to build a system for monitoring of social phenomena, complementing mathematical and information models of the spread of viruses, aimed at minimizing the damage they cause. The feature of developed system is the using of a natural language processing framework based on the associative-ontological approach, and software implementation of the adaptive-behavioral SEIR model, as well as a subsystem for interpreting the collected data, generating metadata for identifying and correcting the model. Conclusions: The proposed system allows to make more balanced management decisions based on the analysis of the current situation in the infosphere. An additional advantage of the system is the ability to identify poorly predictable reactions of society to certain events expressed in media content.

scholarly journals Perceiving Residents’ Festival Activities Based on Social Media Data: A Case Study in Beijing, China

2021 ◽  
Vol 10 (7) ◽  
pp. 474
Author(s):  
Bingqing Wang ◽  
Bin Meng ◽  
Juan Wang ◽  
Siyu Chen ◽  
Jian Liu
Keyword(s):  
Social Media ◽  
Language Processing ◽  
Topic Model ◽  
Central Area ◽  
Classification Model ◽  
Social Media Data ◽  
Ring Road ◽  
Different Types ◽  
Spatial Differences ◽  
Media Data

Social media data contains real-time expressed information, including text and geographical location. As a new data source for crowd behavior research in the era of big data, it can reflect some aspects of the behavior of residents. In this study, a text classification model based on the BERT and Transformers framework was constructed, which was used to classify and extract more than 210,000 residents’ festival activities based on the 1.13 million Sina Weibo (Chinese “Twitter”) data collected from Beijing in 2019 data. On this basis, word frequency statistics, part-of-speech analysis, topic model, sentiment analysis and other methods were used to perceive different types of festival activities and quantitatively analyze the spatial differences of different types of festivals. The results show that traditional culture significantly influences residents’ festivals, reflecting residents’ motivation to participate in festivals and how residents participate in festivals and express their emotions. There are apparent spatial differences among residents in participating in festival activities. The main festival activities are distributed in the central area within the Fifth Ring Road in Beijing. In contrast, expressing feelings during the festival is mainly distributed outside the Fifth Ring Road in Beijing. The research integrates natural language processing technology, topic model analysis, spatial statistical analysis, and other technologies. It can also broaden the application field of social media data, especially text data, which provides a new research paradigm for studying residents’ festival activities and adds residents’ perception of the festival. The research results provide a basis for the design and management of the Chinese festival system.

Looking Back at the Pandemic Onset: Twitter Chatter Reflects Changes in Alcohol Themes

2021 ◽  
Author(s):  
Nicole Ryerson ◽  
Jeffrey Stone
Keyword(s):  
Alcohol Use ◽  
Daily Living ◽  
Early Research ◽  
Work Environments ◽  
Media Content ◽  
Social Phenomena ◽  
Global Pandemic ◽  
Complex Picture ◽  
The Impact ◽  
Looking Back

The COVID-19 global pandemic brought with it massive disruptions across many aspects of daily living including losses of employment and financial opportunities, reduced access to essential resources, lack of engagement in social activities, increases in social isolation, and mass transitions to remote school and work environments. Pre-pandemic research on events with paralleled community-wide effects has demonstrated a resulting increase in alcohol use and misuse as a result of these massive disruptions. However, early research on the impact of the current global pandemic on alcohol use has painted a complex picture. The current study utilized social media content (i.e., Twitter) as a way to investigate the initial impact of the pandemic on our relationship with alcohol. Analyses were also conducted to determine if the pandemic resulted in a shift away from typical weekly patterns related to alcohol use (i.e., increased on weekends vs. weekdays). A 2 (pandemic: pre-pandemic vs. post-pandemic) x 2 (day of week: weekday vs. weekend) ANCOVA was calculated to predict the prevalence of alcohol related tweets while controlling for the total number of tweets. The prevalence of alcohol related tweets significantly increased following the declaration of the global pandemic, however, the pattern of alcohol related tweets across the days of the week did not differ as a result of the pandemic. These results may be a reflection of major shifts in the psychological and social phenomena associated with alcohol as a result of the devastating impacts of the global pandemic.

scholarly journals Mining Social Media Data for Biomedical Signals and Health-Related Behavior

2020 ◽  
Vol 3 (1) ◽  
pp. 433-458 ◽  
Author(s):  
Rion Brattig Correia ◽  
Ian B. Wood ◽  
Johan Bollen ◽  
Luis M. Rocha
Keyword(s):  
Mental Health ◽  
Social Media ◽  
Population Level ◽  
Data Access ◽  
Health Conditions ◽  
Social Phenomena ◽  
Medical Treatments ◽  
Social Media Data ◽  
Health Related ◽  
Media Data

Social media data have been increasingly used to study biomedical and health-related phenomena. From cohort-level discussions of a condition to population-level analyses of sentiment, social media have provided scientists with unprecedented amounts of data to study human behavior associated with a variety of health conditions and medical treatments. Here we review recent work in mining social media for biomedical, epidemiological, and social phenomena information relevant to the multilevel complexity of human health. We pay particular attention to topics where social media data analysis has shown the most progress, including pharmacovigilance and sentiment analysis, especially for mental health. We also discuss a variety of innovative uses of social media data for health-related applications as well as important limitations of social media data access and use.

scholarly journals A Pipeline to Understand Emerging Illness Via Social Media Data Analysis: Case Study on Breast Implant Illness (Preprint)

2021 ◽  
Author(s):  
Vishal Dey ◽  
Peter Krasniak ◽  
Minh Nguyen ◽  
Clara Lee ◽  
Xia Ning
Keyword(s):  
Mental Health ◽  
Social Media ◽  
Natural Language Processing ◽  
Data Analysis ◽  
Natural Language ◽  
Language Processing ◽  
Breast Implant ◽  
Public Attention ◽  
Social Media Data ◽  
Media Data

BACKGROUND A new illness can come to public attention through social media before it is medically defined, formally documented, or systematically studied. One example is a condition known as breast implant illness (BII), which has been extensively discussed on social media, although it is vaguely defined in the medical literature. OBJECTIVE The objective of this study is to construct a data analysis pipeline to understand emerging illnesses using social media data and to apply the pipeline to understand the key attributes of BII. METHODS We constructed a pipeline of social media data analysis using natural language processing and topic modeling. Mentions related to signs, symptoms, diseases, disorders, and medical procedures were extracted from social media data using the clinical Text Analysis and Knowledge Extraction System. We mapped the mentions to standard medical concepts and then summarized these mapped concepts as topics using latent Dirichlet allocation. Finally, we applied this pipeline to understand BII from several BII-dedicated social media sites. RESULTS Our pipeline identified topics related to toxicity, cancer, and mental health issues that were highly associated with BII. Our pipeline also showed that cancers, autoimmune disorders, and mental health problems were emerging concerns associated with breast implants, based on social media discussions. Furthermore, the pipeline identified mentions such as rupture, infection, pain, and fatigue as common self-reported issues among the public, as well as concerns about toxicity from silicone implants. CONCLUSIONS Our study could inspire future studies on the suggested symptoms and factors of BII. Our study provides the first analysis and derived knowledge of BII from social media using natural language processing techniques and demonstrates the potential of using social media information to better understand similar emerging illnesses. CLINICALTRIAL

scholarly journals Using social media data to map the areas most affected by ISIS in Syria

2020 ◽  
Vol 26 (1) ◽  
pp. 464-470
Author(s):  
Mohamad Hasan
Keyword(s):  
Social Media ◽  
Language Processing ◽  
Geographical Information ◽  
Data Mapping ◽  
Islamic State ◽  
Social Media Data ◽  
The Social ◽  
Mapping Process ◽  
Data Source ◽  
Media Data

This paper presents a model to collect, save, geocode, and analyze social media data. The model is used to collect and process the social media data concerned with the ISIS terrorist group (the Islamic State in Iraq and Syria), and to map the areas in Syria most affected by ISIS accordingly to the social media data. Mapping process is assumed automated compilation of a density map for the geocoded tweets. Data mined from social media (e.g., Twitter and Facebook) is recognized as dynamic and easily accessible resources that can be used as a data source in spatial analysis and geographical information system. Social media data can be represented as a topic data and geocoding data basing on the text of the mined from social media and processed using Natural Language Processing (NLP) methods. NLP is a subdomain of artificial intelligence concerned with the programming computers to analyze natural human language and texts. NLP allows identifying words used as an initial data by developed geocoding algorithm. In this study, identifying the needed words using NLP was done using two corpora. First corpus contained the names of populated places in Syria. The second corpus was composed in result of statistical analysis of the number of tweets and picking the words that have a location meaning (i.e., schools, temples, etc.). After identifying the words, the algorithm used Google Maps geocoding API in order to obtain the coordinates for posts.

Using Big Data Opinion Mining to Predict Rises and Falls in the Stock Price Index

2015 ◽  
pp. 30-42
Author(s):  
Yoosin Kim ◽  
Michelle Jeong ◽  
Seung Ryul Jeong
Keyword(s):  
Big Data ◽  
Language Processing ◽  
Stock Price ◽  
Opinion Mining ◽  
Predictive Accuracy ◽  
Online News ◽  
Social Phenomena ◽  
Price Increases ◽  
Novel Approach ◽  
Investment Decision Making

In light of recent research that has begun to examine the link between textual “big data” and social phenomena such as stock price increases, this chapter takes a novel approach to treating news as big data by proposing the intelligent investment decision-making support model based on opinion mining. In an initial prototype experiment, the researchers first built a stock domain-specific sentiment dictionary via natural language processing of online news articles and calculated sentiment scores for the opinions extracted from those stories. In a separate main experiment, the researchers gathered 78,216 online news articles from two different media sources to not only make predictions of actual stock price increases but also to compare the predictive accuracy of articles from different media sources. The study found that opinions that are extracted from the news and treated with proper sentiment analysis can be effective in predicting changes in the stock market.

Spiritual Exercise as Techne: Philosophy as a Way of Liberating Education

2021 ◽  
Vol 25 (4) ◽  
pp. 640-655
Author(s):  
Shu-Fen Lin ◽  
Wei-Ding Tsai ◽  
Denis Igorevich Chistyakov
Keyword(s):  
Education System ◽  
Modern Society ◽  
Modern Technology ◽  
Spiritual Exercises ◽  
Social Phenomena ◽  
Modern Education ◽  
Current State ◽  
Life Context ◽  
Meaningful Existence

The study of education systems as social phenomena has led scholars to question the role of education in modern society. The question of how to improve education naturally leads to concerns about what is wrong with the present education system. If education is meant to elevate the next generation, how can it meet the goal of ensuring a meaningful existence for those being educated? Scholars have demonstrated that education has been reduced to a process of the construction of objects, where curriculum as techne commodifies students into products with market value. We propose that the tendency of interpreting techne as technology is a perspective of the modern age, and the rules of modern education are based on the rules of modern technology, under the guidance of the paradigm of productivity. We will introduce a broader interpretation of techne which frames it as the cultivation of virtue, i.e., virtue-techne. On this basis, education could be viewed as techne in the sense of praxis (practice, exercise), rather than as fabrication in the sense of production. We highlight the rising rate of student suicides in Taiwan in recent years, where we determine the education system lacks a focus on praxis. This article investigates alternative praxis-oriented notions of education, from Aristotle's cultivation of virtue to Hadot's "spiritual exercises," to advocate for a shift away from the production paradigm. Indebted to Heidegger, we clarify his "techne as revealing" by emphasizing two frameworks for education: The first, modern education being valued by its adherence to metrics based in the paradigm of production. The second, education as a process wherein its value is derived from the life context of the participating individual. Finally, as a comparative study, we explore the current state of education in Russia and Taiwan, and present the case of one high school in Taiwan which has adopted the practice of spiritual exercises in its curriculum, including a required hike to the peak of Taiwan's tallest mountain, to cultivate a sense of (and value for) the liberated life before its students graduate.

TERRITORIAL COMMUNITIES IN A SPACE OF A METROPOLIS. ON THE QUESTION OF TERMS

2020 ◽  
pp. 102-111
Author(s):  
Evgenia I. Gromova ◽  
◽  
Alexandra O. Lazukina ◽  
Valeria I. Terentieva ◽  
◽  
...  
Keyword(s):  
Scientific Literature ◽  
Modern Society ◽  
Social Processes ◽  
Special Significance ◽  
Social Phenomena ◽  
The Social

The article analyzes scientific literature on the topic of the significance of the transformation of territorial communities in the space of a metropolis. It is shown that there are systemic difficulties in the analysis of a number of social phenomena due to the lack of generally accepted formulations of such concepts as “territorial communities”, “megalopolis space” and the differences between the concepts of “territory” and “space”. It is concluded that the aforementioned definitions should be determined by the social processes that occur in modern society, since today they acquire special significance as independent scientific categories in assessing both individual events caused by short-sighted decisions of the authorities and growing negative social phenomena in the form of protest behaviors that result from them.

Who’s Tweeting About the President? What Big Survey Data Can Tell Us About Digital Traces?

2019 ◽  
Vol 38 (5) ◽  
pp. 633-650 ◽  
Author(s):  
Josh Pasek ◽  
Colleen A. McClain ◽  
Frank Newport ◽  
Stephanie Marken
Keyword(s):  
Social Media ◽  
Survey Data ◽  
Large Scale ◽  
Presidential Approval ◽  
Social Phenomena ◽  
Social Media Data ◽  
Complex Picture ◽  
Large Scale Survey ◽  
Media Data

Researchers hoping to make inferences about social phenomena using social media data need to answer two critical questions: What is it that a given social media metric tells us? And who does it tell us about? Drawing from prior work on these questions, we examine whether Twitter sentiment about Barack Obama tells us about Americans’ attitudes toward the president, the attitudes of particular subsets of individuals, or something else entirely. Specifically, using large-scale survey data, this study assesses how patterns of approval among population subgroups compare to tweets about the president. The findings paint a complex picture of the utility of digital traces. Although attention to subgroups improves the extent to which survey and Twitter data can yield similar conclusions, the results also indicate that sentiment surrounding tweets about the president is no proxy for presidential approval. Instead, after adjusting for demographics, these two metrics tell similar macroscale, long-term stories about presidential approval but very different stories at a more granular level and over shorter time periods.

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