scholarly journals Analysis of methods for determining the tonality of text data of a user of social networks

2021 ◽  
Author(s):  
A.A Zotkina ◽  
◽  
A.I Martyshkin ◽  
V.V Kuzina ◽  
◽  
...  
Keyword(s):  
Social Networks ◽  
Text Data

scholarly journals Determining the proximity of groups in social networks based on text analysis using big data

2019 ◽  
pp. 521-526
Author(s):  
A S Mukhin ◽  
I A Rytsarev ◽  
R A Paringer ◽  
A V Kupriyanov ◽  
D V Kirsh
Keyword(s):  
Social Networks ◽  
Big Data ◽  
Social Network ◽  
Data Collection ◽  
Text Analysis ◽  
Software Tool ◽  
Text Data ◽  
The Social ◽  
Information Research ◽  
Definition Of

The article is devoted to the definition of such groups in social networks. The object of the study was selected data social network Vk. Text data was collected, processed and analyzed. To solve the problem of obtaining the necessary information, research was conducted in the field of optimization of data collection of the social network Vk. A software tool that provides the collection and subsequent processing of the necessary data from the specified resources has been developed. The existing algorithms of text analysis, mainly of large volume, were investigated and applied.

THE EMOTION OF JOY IN SOCIAL NETWORKS: VERBAL, PARAVERBAL AND NONVERBAL FORMS OF EXPRESSION

2021 ◽  
Author(s):  
A.V. Kolmogorova ◽  
S.R. Akhmadeeva
Keyword(s):  
Social Networks ◽  
Comparative Analysis ◽  
Team Sport ◽  
Sport Performance ◽  
Emotion Expression ◽  
The Internet ◽  
Text Data ◽  
Gender Factor

The article explores the text data of the Internet-comments published on social networks by fans to celebrate the victory of their favorite sportsmen. The aim of the publication is to analyze verbal, paraverbal and nonverbal forms of emotion expression in two groups of fans: those who are keen on sports profiling typically masculine properties (strength, audacity, endurance), and, on the contrary, those who are passionate about the sport performance featuring feminine characteristics (grace, beauty, flexibility). The conducted comparative analysis gives evidence about the presence of a number of specific features due to the effect of gender factor. However, this factor largely correlates with other variables, such as linguacultural patterns, the nature of the sport itself (team sport vs individual sport).

scholarly journals Learning social networks from text data using covariate information

2021 ◽  
Author(s):  
Xiaoyi Yang ◽  
Nynke M. D. Niezink ◽  
Rebecca Nugent
Keyword(s):  
Social Networks ◽  
Conditional Independence ◽  
Graphical Model ◽  
Text Data ◽  
Graphical Lasso ◽  
Network Recovery ◽  
Covariate Information ◽  
The People ◽  
Historical Figures ◽  
Independence Structure

AbstractAccurately describing the lives of historical figures can be challenging, but unraveling their social structures perhaps is even more so. Historical social network analysis methods can help in this regard and may even illuminate individuals who have been overlooked by historians, but turn out to be influential social connection points. Text data, such as biographies, are a useful source of information for learning historical social networks but the identifcation of links based on text data can be challenging. The Local Poisson Graphical Lasso model models social networks by conditional independence structures, and leverages the number of name co-mentions in the text to infer relationships. However, this method does not take into account the abundance of covariate information that is often available in text data. Conditional independence structure like Poisson Graphical Model, which makes use name mention counts in the text can be useful tools to avoid false positive links due to the co-mentions but given historical tendency of frequently used or common names, without additional distinguishing information, we may introduce incorrect connections. In this work, we therefore extend the Local Poisson Graphical Lasso model with a (multiple) penalty structure that incorporates covariates, opening up the opportunity for similar individuals to have a higher probability of being connected. We propose both greedy and Bayesian approaches to estimate the penalty parameters. We present results on data simulated with characteristics of historical networks and show that this type of penalty structure can improve network recovery as measured by precision and recall. We also illustrate the approach on biographical data of individuals who lived in early modern Britain between 1500 and 1575. We will show how these covariates affect the statistical model’s performance using simulations, discuss how it helps to better identify links for the people with common names and those who are traditionally underrepresented in the biography text data.

scholarly journals Automatic Methods for Detecting Cultural Bias in Social Media (Based on Telegram’s Dialogs)

2021 ◽  
Vol 19 (2) ◽  
pp. 54-72
Author(s):  
Y. A. Zherebtsova ◽  
A. V. Chizhik ◽  
A. P. Sadokhin
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Social Group ◽  
Cultural Bias ◽  
Text Data ◽  
Social Surveys ◽  
Cultural Behavior ◽  
Semantic Associations ◽  
Automatic Methods ◽  
Cultural Shifts

In this paper, we described and tested several ways to use machine learning in order to analyze large collections of text data from social networks (namely, public Telegram chat), retrieve relevant social or cultural information from them, and to visualize the results of the research. The proposed approach has an advantage to reveal hidden patterns of social, political or cultural behavior by being able to cover large amounts of data. It can complement the standard social surveys methodology. Automatic detecting cultural bias on the example of social media requires mastering methods for measuring and visualizing its different kinds, such as cultural shifts, specific national or group refractions, mutations, stereotypes. We argue that cultural bias is a result of nonrandom errors in thinking. It is based, firstly, on a person's understanding of himself and the world around him and, secondly, on the translation of this understanding into abstraction in the form of common misconceptions, ideologemes, narrative, slogans. In society the bias inevitably leads to the separation of one social group or subculture from another. Social networks (both classic and new formats, for example, messengers with public chat options) are the most active ground for the representation of this phenomenon. Since the discussion of sociopolitical and cultural contexts in the case of chats takes place in public, the participants of such a communicative act tend to get approval of the social group to which they are ideologically close. It is this phenomenon that allows us to form comparisons of the “friend - foe” type, which lead next to unconscious cultural shifts. Thus, mastering methods to identify properly cultural shifts is not only relevant but crucial for the intra- and intercultural communication, for controlling the level of aggressiveness of the society, understanding its mood. As helpful illustrations, readers will find semantic associations elicited by the words “freedom”, “democracy”, “Internet”; sociocultural analysis of several topical clusters (e.g. Россия, страна, Путин, русский, православный); visualization of semantic associations for the words “freedom”, “democracy”, “Internet”.

scholarly journals Hot Topic Community Discovery on Cross Social Networks

2019 ◽  
Vol 11 (3) ◽  
pp. 60 ◽  
Author(s):  
Xuan Wang ◽  
Bofeng Zhang ◽  
Furong Chang
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Online Social Networks ◽  
Latent Dirichlet Allocation ◽  
Rapid Development ◽  
Unified Model ◽  
Text Data ◽  
Community Discovery ◽  
Latent Topic ◽  
Discovery Method

The rapid development of online social networks has allowed users to obtain information, communicate with each other and express different opinions. Generally, in the same social network, users tend to be influenced by each other and have similar views. However, on another social network, users may have opposite views on the same event. Therefore, research undertaken on a single social network is unable to meet the needs of research on hot topic community discovery. “Cross social network” refers to multiple social networks. The integration of information from multiple social network platforms forms a new unified dataset. In the dataset, information from different platforms for the same event may contain similar or unique topics. This paper proposes a hot topic discovery method on cross social networks. Firstly, text data from different social networks are fused to build a unified model. Then, we obtain latent topic distributions from the unified model using the Labeled Biterm Latent Dirichlet Allocation (LB-LDA) model. Based on the distributions, similar topics are clustered to form several topic communities. Finally, we choose hot topic communities based on their scores. Experiment result on data from three social networks prove that our model is effective and has certain application value.

scholarly journals Influence of Personal Preferences on Link Dynamics in Social Networks

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Ashwin Bahulkar ◽  
Boleslaw K. Szymanski ◽  
Nitesh Chawla ◽  
Omar Lizardo ◽  
Kevin Chan
Keyword(s):  
Social Networks ◽  
Social Issues ◽  
Cognitive Networks ◽  
Text Data ◽  
Student Preferences ◽  
Political Views ◽  
Wide Range ◽  
Self Reports ◽  
Study Participants ◽  
Link Formation

We study a unique network dataset including periodic surveys and electronic logs of dyadic contacts via smartphones. The participants were a sample of freshmen entering university in the Fall 2011. Their opinions on a variety of political and social issues and lists of activities on campus were regularly recorded at the beginning and end of each semester for the first three years of study. We identify a behavioral network defined by call and text data, and a cognitive network based on friendship nominations in ego-network surveys. Both networks are limited to study participants. Since a wide range of attributes on each node were collected in self-reports, we refer to these networks as attribute-rich networks. We study whether student preferences for certain attributes of friends can predict formation and dissolution of edges in both networks. We introduce a method for computing student preferences for different attributes which we use to predict link formation and dissolution. We then rank these attributes according to their importance for making predictions. We find that personal preferences, in particular political views, and preferences for common activities help predict link formation and dissolution in both the behavioral and cognitive networks.

scholarly journals Challenges of event detection from social media streams

2018 ◽  
Vol 7 (2.15) ◽  
pp. 72
Author(s):  
Wafa Zubair Al-Dyani ◽  
Adnan Hussein Yahya ◽  
Farzana Kabir Ahmad
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Event Detection ◽  
Text Data ◽  
Wide Range ◽  
Noise Data ◽  
Social Media Platforms ◽  
Use Of Social Media ◽  
High Level ◽  
Privacy Issues

The area of Event Detection (ED) has attracted researchers' attention over the last few years because of the wide use of social media.  Many studies have examined the problem of ED in various social media platforms, like Twitter, Facebook, YouTube, etc. The ED task for social networks involves many issues, including the processing of huge volumes of data with a high level of noise, data collection and privacy issues, etc.  Hence, this article discusses and presents the wide range of challenges encountered in the ED process from unstructured text data for the most popular Social Networks (SNs), such as Facebook and Twitter. The main goal is to aid the researchers to understand the main challenges and to discuss the future directions in the ED area. 

scholarly journals Influential post identification on Instagram through caption and hashtag analysis

2020 ◽  
Vol 53 (3-4) ◽  
pp. 409-415 ◽  
Author(s):  
Benyamin Bashari ◽  
Ehsan Fazl-Ersi
Keyword(s):  
Social Networks ◽  
Support Vector Machines ◽  
Significant Role ◽  
Network Topology ◽  
Support Vector ◽  
Alternative Source ◽  
Text Data ◽  
Vector Machines ◽  
Source Of Information ◽  
Embedding Methods

Influencer marketing through social networks is becoming an important alternative to traditional ways of advertising. Various solutions have been proposed that often take advantage of graph-based approaches to discover influencers in social networks. This paper designs a new method for the discovery of influential users in Instagram, by focusing on user-generated posts as an alternative source of information, to potentially augment the existing solutions based on network topology or connections. The text associated with each Instagram post potentially consists of a set of hashtags and a descriptive caption. Various word embedding methods such as Co-occurrence and fastText are examined to represent captions and hashtags. These representations are combined within a support vector machines framework to distinguish influential posts from non-influential ones. Extensive experiments show that the text data can play a significant role in identifying influential posts, and further demonstrate the strength of the proposed method for discovering influencers on Instagram.

THE EMOTION OF JOY IN SOCIAL NETWORKS: VERBAL, PARAVERBAL AND NONVERBAL FORMS OF EXPRESSION

2021 ◽  
Author(s):  
A.V. Kolmogorova ◽  
S.R. Akhmadeeva
Keyword(s):  
Social Networks ◽  
Comparative Analysis ◽  
Team Sport ◽  
Sport Performance ◽  
Emotion Expression ◽  
The Internet ◽  
Text Data ◽  
Gender Factor

The article explores the text data of the Internet-comments published on social networks by fans to celebrate the victory of their favorite sportsmen. The aim of the publication is to analyze verbal, paraverbal and nonverbal forms of emotion expression in two groups of fans: those who are keen on sports profiling typically masculine properties (strength, audacity, endurance), and, on the contrary, those who are passionate about the sport performance featuring feminine characteristics (grace, beauty, flexibility). The conducted comparative analysis gives evidence about the presence of a number of specific features due to the effect of gender factor. However, this factor largely correlates with other variables, such as linguacultural patterns, the nature of the sport itself (team sport vs individual sport).

scholarly journals An approach to the automatic linguistic ontology construction to determine the interests of social networks users

2021 ◽  
Vol 11 (3) ◽  
pp. 351-363
Author(s):  
A.M. Namestnikov ◽  
◽  
N.D. Pirogova ◽  
A.A. Filippov
Keyword(s):  
Social Networks ◽  
Subject Area ◽  
Knowledge Graph ◽  
Writing Style ◽  
Text Data ◽  
Ontology Construction ◽  
Running Time ◽  
Automatic Construction ◽  
The Subject ◽  
Linguistic Ontology

Social networks provide researchers with the opportunity to obtain an array of text data for further analysis within a certain subject area. Each subject area has its own specific professional vocabulary and writing style. When defining the subject area of text material there is a big problem with building dictionaries, thesauri, and ontologies. In this article a linguistic ontology is considered under ontology and which is aimed to determine the subject area of text material. An algorithm for the automatic construction of an ontology based on the Wikidata knowledge graph is presented. The task is to map a set of objects of the Wikidata knowledge graph to a set of entities of a linguistic ontology. The article pro-poses an algorithm for determining the degree of belonging of the text material to the subject area. Experiments on assessing the time of building an ontology and the applicability of the obtained linguistic ontologies to the problem of determining the degree of belonging of text materials in the subject area have shown: the running time of the algorithm and the number of terms in the formed ontology are directly proportional to the number of analyzed properties and Wikidata objects; the formed linguistic ontology is applicable to the problem of determining the degree of belonging of a text to a subject area

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