scholarly journals Evolution of diversity and dominance of companies in online activity

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0249993
Author(s):  
Paul X. McCarthy ◽  
Xian Gong ◽  
Sina Eghbal ◽  
Daniel S. Falster ◽  
Marian-Andrei Rizoiu
Keyword(s):  
Social Media ◽  
Large Scale ◽  
Preferential Attachment ◽  
Online Activity ◽  
Enterprise Value ◽  
Online Social Media ◽  
Long Run ◽  
User Attention ◽  
Online Attention ◽  
The Web

Ever since the web began, the number of websites has been growing exponentially. These websites cover an ever-increasing range of online services that fill a variety of social and economic functions across a growing range of industries. Yet the networked nature of the web, combined with the economics of preferential attachment, increasing returns and global trade, suggest that over the long run a small number of competitive giants are likely to dominate each functional market segment, such as search, retail and social media. Here we perform a large scale longitudinal study to quantify the distribution of attention given in the online environment to competing organisations. In two large online social media datasets, containing more than 10 billion posts and spanning more than a decade, we tally the volume of external links posted towards the organisations’ main domain name as a proxy for the online attention they receive. We also use the Common Crawl dataset—which contains the linkage patterns between more than a billion different websites—to study the patterns of link concentration over the past three years across the entire web. Lastly, we showcase the linking between economic, financial and market data by exploring the relationships between online attention on social media and the growth in enterprise value in the electric carmaker Tesla. Our analysis shows that despite the fact that we observe consistent growth in all the macro indicators—the total amount of online attention, in the number of organisations with an online presence, and in the functions they perform—we also observe that a smaller number of organisations account for an ever-increasing proportion of total user attention, usually with one large player dominating each function. These results highlight how evolution of the online economy involves innovation, diversity, and then competitive dominance.

Concrete Monsters of the Welfare State: Discussions of Brutalist Architecture on Social Media

2021 ◽  
pp. 120633122110193
Author(s):  
Max Holleran
Keyword(s):  
Social Media ◽  
Welfare State ◽  
Large Scale ◽  
Eastern European ◽  
Municipal Governments ◽  
Online Social Media ◽  
1960S And 1970S ◽  
The 1960S ◽  
The Aesthetic ◽  
The Welfare State

Brutalist architecture is an object of fascination on social media that has taken on new popularity in recent years. This article, drawing on 3,000 social media posts in Russian and English, argues that the buildings stand out for their arresting scale and their association with the expanding state in the 1960s and 1970s. In both North Atlantic and Eastern European contexts, the aesthetic was employed in publicly financed urban planning projects, creating imposing concrete structures for universities, libraries, and government offices. While some online social media users associate the style with the overreach of both socialist and capitalist governments, others are more nostalgic. They use Brutalist buildings as a means to start conversations about welfare state goals of social housing, free university, and other services. They also lament that many municipal governments no longer have the capacity or vision to take on large-scale projects of reworking the built environment to meet contemporary challenges.

scholarly journals A new approach of ranking prevalent news topics from social media using unsupervised techniques

2019 ◽  
Vol 8 (07) ◽  
pp. 24683-24789
Author(s):  
Dr. D. Murali ◽  
Vinutha BA
Keyword(s):  
Social Media ◽  
News Media ◽  
User Interaction ◽  
Common People ◽  
New Approach ◽  
Online Social Media ◽  
The Social ◽  
User Attention ◽  
The Common ◽  
Filter Noise

The precious data from online origin has developed into a extended research. The mass media and news media provides the daily events to the common people. Huge amount of information is been achieved by an online social media suchlike Twitter, which contains more information about news-associated content. It is necessary to find a way to filter noise, for these resources to be useful and grab the content that is depend on the similarity to news media. Despite after the noise is eliminated the excessive data still remain in the data so it is essential to prioritize it for utilization. We are introducing three factors for prioritization. The unsupervised technique finds the news topics that are common in the pair of social media and news media, and then ranks them by the applicability factors such as MF, UA and UI. Initially the temporal prevalence of the appropriate topic in news media focus (MF). Secondary the temporal prevalence of the appropriate topic in social media illustrates the user attention (UA). Finally the interconnection among the social media users who specify this topic demonstrates the power of the society who is discussing; it is termed as the user interaction (UI).  

scholarly journals MO’ CHARACTERS MO’ PROBLEMS: ONLINE SOCIAL MEDIA PLATFORM CONSTRAINTS AND MODES OF COMMUNICATION

2020 ◽  
Author(s):  
Lewis Mitchell ◽  
Joshua Dent ◽  
Joshua Ross
Keyword(s):  
Social Media ◽  
Language Processing ◽  
Large Scale ◽  
English Language ◽  
Length Change ◽  
Reading Level ◽  
Online Social Media ◽  
Social Media Platforms ◽  
Media Platform ◽  
Processing Techniques

It is widely accepted that different online social media platforms produce different modes of communication, however the ways in which these modalities are shaped by the constraints of a particular platform remain difficult to quantify. On 7 November 2017 Twitter doubled the character limit for users to 280 characters, presenting a unique opportunity to study the response of this population to an exogenous change to the communication medium. Here we analyse a large dataset comprising 387 million English-language tweets (10% of all public tweets) collected over the September 2017--January 2018 period to quantify and explain large-scale changes in individual behaviour and communication patterns precipitated by the character-length change. Using statistical and natural language processing techniques we find that linguistic complexity increased after the change, with individuals writing at a significantly higher reading level. However, we find that some textual properties such as statistical language distribution remain invariant across the change, and are no different to writings in different online media. By fitting a generative mathematical model to the data we find a surprisingly slow response of the Twitter population to this exogenous change, with a substantial number of users taking a number of weeks to adjust to the new medium. In the talk we describe the model and Bayesian parameter estimation techniques used to make these inferences. Furthermore, we argue for mathematical models as an alternative exploratory methodology for "Big" social media datasets, empowering the researcher to make inferences about the human behavioural processes which underlie large-scale patterns and trends.

MidSemI

2019 ◽  
Vol 10 (2) ◽  
pp. 1-25 ◽  
Author(s):  
Samir Sellami ◽  
Taoufiq Dkaki ◽  
Nacer Eddine Zarour ◽  
Pierre-Jean Charrel
Keyword(s):  
Social Media ◽  
Linked Data ◽  
Large Scale ◽  
Keyword Search ◽  
Evaluation Study ◽  
Added Value ◽  
Web Of Data ◽  
Integration Techniques ◽  
User Friendly ◽  
The Web

The web diversification into the Web of Data and social media means that companies need to gather all the necessary data to help make the best-informed market decisions. However, data providers on the web publish data in various data models and may equip it with different search capabilities, thus requiring data integration techniques to access them. This work explores the current challenges in this area, discusses the limitations of some existing integration tools, and addresses them by proposing a semantic mediator-based approach to virtually integrate enterprise data with large-scale social and linked data. The implementation of the proposed approach is a configurable middleware application and a user-friendly keyword search interface that retrieves its input from internal enterprise data combined with various SPARQL endpoints and Web APIs. An evaluation study was conducted to compare its features with recent integration approaches. The results illustrate the added value and usability of the contributed approach.

scholarly journals A survey of large-scale reasoning on the Web of data

2018 ◽  
Vol 33 ◽  
Author(s):  
Grigoris Antoniou ◽  
Sotiris Batsakis ◽  
Raghava Mutharaju ◽  
Jeff Z. Pan ◽  
Guilin Qi ◽  
...  
Keyword(s):  
Systematic Review ◽  
Social Media ◽  
Parallel Algorithms ◽  
High Performance ◽  
Large Scale ◽  
Open Problems ◽  
Reasoning Systems ◽  
Web Of Data ◽  
Computational Properties ◽  
The Web

AbstractAs more and more data is being generated by sensor networks, social media and organizations, the Web interlinking this wealth of information becomes more complex. This is particularly true for the so-called Web of Data, in which data is semantically enriched and interlinked using ontologies. In this large and uncoordinated environment, reasoning can be used to check the consistency of the data and of associated ontologies, or to infer logical consequences which, in turn, can be used to obtain new insights from the data. However, reasoning approaches need to be scalable in order to enable reasoning over the entire Web of Data. To address this problem, several high-performance reasoning systems, which mainly implement distributed or parallel algorithms, have been proposed in the last few years. These systems differ significantly; for instance in terms of reasoning expressivity, computational properties such as completeness, or reasoning objectives. In order to provide a first complete overview of the field, this paper reports a systematic review of such scalable reasoning approaches over various ontological languages, reporting details about the methods and over the conducted experiments. We highlight the shortcomings of these approaches and discuss some of the open problems related to performing scalable reasoning.

scholarly journals Identifying Malicious Accounts in Social Media Based on Online Promotions

2020 ◽  
Vol 9 (6) ◽  
pp. 276-280
Keyword(s):  
Social Media ◽  
False Positive ◽  
Limited Time ◽  
Web Based ◽  
Online Social Media ◽  
Number Of Individuals ◽  
The Web ◽  
New Framework

There is a developing number of individuals who hold accounts via web-based networking media stages however conceal their character for pernicious purposes. Tragically, almost no research has been done to date to distinguish counterfeit characters made by people, particularly so via web-based networking media stages. Online social media step by step incorporate monetary capacities by permitting the utilization of genuine and virtual money, filling in as new stages to have an assortment of organizations, for example, online limited time occasions, where clients can become virtual assesses as a compensation for going to such occasions. Both NSOs and business stakeholders are essentially concerned when Attackers actualize an assortment of records to gather virtual money from these occasions, making these occasions insufficient and the outcome in critical budgetary misfortune. It turns out to be critical to proactively identify these malignant records before on the web and special exercises in this manner they decline their need to be remunerated. In this paper, we have present a new framework, called ProGuard, for accomplish this by deliberately coordinating highlights that describe accounts from three viewpoints, including their overall conduct, their top-up designs and the utilization of their cash. We directed various experiments dependent on information gathered by Ten cent QQ, a world chief OSN with coordinated budgetary administration exercises. Exploratory outcomes have indicated that our framework is equipped for accomplishing a high recognition pace of 96.67% at a low false positive pace of 0.3%.

scholarly journals A tool for tracking the propagation of words on Reddit

2021 ◽  
Vol 3 (1) ◽  
pp. 117-132
Author(s):  
Tom Willaert ◽  
Paul Van Eecke ◽  
Jeroen Van Soest ◽  
Katrien Beuls
Keyword(s):  
Social Media ◽  
Information Diffusion ◽  
Computational Social Science ◽  
Data Driven ◽  
Online Media ◽  
Online Tool ◽  
Online Social Media ◽  
Social Media Platforms ◽  
Over Time ◽  
The Web

Abstract The data-driven study of cultural information diffusion in online (social) media is currently an active area of research. The availability of data from the web thereby generates new opportunities to examine how words propagate through online media and communities, as well as how these diffusion patterns are intertwined with the materiality and culture of social media platforms. In support of such efforts, this paper introduces an online tool for tracking the consecutive occurrences of words across subreddits on Reddit between 2005 and 2017. By processing the full Pushshift.io Reddit comment archive for this period (Baumgartner et al., 2020), we are able to track the first occurrences of 76 million words, allowing us to visualize which subreddits subsequently adopt any of those words over time. We illustrate this approach by addressing the spread of terms referring to famous internet controversies, and the percolation of alt-right terminology. By making our instrument and the processed data publically available, we aim to facilitate a range of exploratory analyses in computational social science, the digital humanities, and related fields.

scholarly journals Analyzing gender inequality through large-scale Facebook advertising data

2018 ◽  
Vol 115 (27) ◽  
pp. 6958-6963 ◽  
Author(s):  
David Garcia ◽  
Yonas Mitike Kassa ◽  
Angel Cuevas ◽  
Manuel Cebrian ◽  
Esteban Moro ◽  
...  
Keyword(s):  
Social Media ◽  
Gender Equality ◽  
Gender Inequality ◽  
Gender Gap ◽  
Online Social Networks ◽  
Large Scale ◽  
Added Value ◽  
Transformative Power ◽  
Online Social Media ◽  
Facebook Advertising

Online social media are information resources that can have a transformative power in society. While the Web was envisioned as an equalizing force that allows everyone to access information, the digital divide prevents large amounts of people from being present online. Online social media, in particular, are prone to gender inequality, an important issue given the link between social media use and employment. Understanding gender inequality in social media is a challenging task due to the necessity of data sources that can provide large-scale measurements across multiple countries. Here, we show how the Facebook Gender Divide (FGD), a metric based on aggregated statistics of more than 1.4 billion users in 217 countries, explains various aspects of worldwide gender inequality. Our analysis shows that the FGD encodes gender equality indices in education, health, and economic opportunity. We find gender differences in network externalities that suggest that using social media has an added value for women. Furthermore, we find that low values of the FGD are associated with increases in economic gender equality. Our results suggest that online social networks, while suffering evident gender imbalance, may lower the barriers that women have to access to informational resources and help to narrow the economic gender gap.

scholarly journals Efficient Computation of the Well-Founded Semantics over Big Data

2014 ◽  
Vol 14 (4-5) ◽  
pp. 445-459 ◽  
Author(s):  
ILIAS TACHMAZIDIS ◽  
GRIGORIS ANTONIOU ◽  
WOLFGANG FABER
Keyword(s):  
Social Media ◽  
Big Data ◽  
Logic Programming ◽  
Large Scale ◽  
Nonmonotonic Reasoning ◽  
Efficient Computation ◽  
Mapreduce Framework ◽  
The Face ◽  
Complex Knowledge ◽  
The Web

AbstractData originating from the Web, sensor readings and social media result in increasingly huge datasets. The so called Big Data comes with new scientific and technological challenges while creating new opportunities, hence the increasing interest in academia and industry. Traditionally, logic programming has focused on complex knowledge structures/programs, so the question arises whether and how it can work in the face of Big Data. In this paper, we examine how the well-founded semantics can process huge amounts of data through mass parallelization. More specifically, we propose and evaluate a parallel approach using the MapReduce framework. Our experimental results indicate that our approach is scalable and that well-founded semantics can be applied to billions of facts. To the best of our knowledge, this is the first work that addresses large scale nonmonotonic reasoning without the restriction of stratification for predicates of arbitrary arity.

scholarly journals An experimental study on feature engineering and learning approaches for aggression detection in social media

2019 ◽  
Vol 22 (63) ◽  
pp. 81-100 ◽  
Author(s):  
Antonela Tommasel ◽  
Juan Manuel Rodriguez ◽  
Daniela Godoy
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Social Relations ◽  
Large Scale ◽  
Well Being ◽  
Feature Engineering ◽  
Learning Approaches ◽  
Social Media Networks ◽  
Online Social Media ◽  
Learning Techniques

With the widespread of modern technologies and social media networks, a new form of bullying occurring anytime and anywhere has emerged. This new phenomenon, known as cyberaggression or cyberbullying, refers to aggressive and intentional acts aiming at repeatedly causing harm to other person involving rude, insulting, offensive, teasing or demoralising comments through online social media. As these aggressions represent a threatening experience to Internet users, especially kids and teens who are still shaping their identities, social relations and well-being, it is crucial to understand how cyberbullying occurs to prevent it from escalating. Considering the massive information on the Web, the developing of intelligent techniques for automatically detecting harmful content is gaining importance, allowing the monitoring of large-scale social media and the early detection of unwanted and aggressive situations. Even though several approaches have been developed over the last few years based both on traditional and deep learning techniques, several concerns arise over the duplication of research and the difficulty of comparing results. Moreover, there is no agreement regarding neither which type of technique is better suited for the task, nor the type of features in which learning should be based. The goal of this work is to shed some light on the effects of learning paradigms and feature engineering approaches for detecting aggressions in social media texts. In this context, this work provides an evaluation of diverse traditional and deep learning techniques based on diverse sets of features, across multiple social media sites. 

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