scholarly journals CloutContracts Whitepaper

2021 ◽  
Author(s):  
Andrew Kamal
Keyword(s):  
Social Media ◽  
High Performance ◽  
Large Scale ◽  
Social Aspects ◽  
Smart Contracts ◽  
New Class ◽  
Social Media Platforms ◽  
Ease Of Access ◽  
Large Scale Networks ◽  
Basic Level

CloutContracts is a smart contracts layer on top of and complimentary to BitClout, as well as potentially other social media platforms in the future as well. As a smart contracts layer, many creators onboarded to CloutContracts can create high performance DAPPs w/ an emphasis on low gas fees, customization, speed and various social aspects as well. This will eventually allow creators to build large scale networks, tokenization usecases, and bring blockchain adaptability to their fanbases. Unlike traditional rollup networks or DAPP tools, the emphasis is on the creator, adaptability, and expanded functionalities such as modular tools or microservices. CloutContracts aims to have creators feel like they aren't needing to choose between expanded functionality from running their own blockchain or accessibility from running on top of a network. This creates a new class of blockchain developers, in which ease of access is aimed towards both the most basic level to running complex lightweight apps on JavaScript or Solidity.

Physical Distancing and Social Media Use in Emerging and Older Adults During COVID-19: Large Scale Cross-Sectional and Longitudinal Survey Study (Preprint)

2021 ◽  
Author(s):  
Thabo J van Woudenberg ◽  
Roy Hendrikx ◽  
Moniek Buijzen ◽  
Julia CM van Weert ◽  
Bas van den Putte ◽  
...  
Keyword(s):  
Older Adults ◽  
Social Media ◽  
Emerging Adults ◽  
Large Scale ◽  
Media Use ◽  
Longitudinal Survey ◽  
Social Media Use ◽  
Random Intercept ◽  
Social Media Platforms

BACKGROUND Although emerging adults play a role in the spread of COVID-19, they are less likely to develop severe symptoms after infection. Emerging adults’ relatively high use of social media as source of information raises concerns regarding COVID-19 related behavioral compliance (i.e., physical distancing) in this age group. OBJECTIVE Therefore, the current study investigated physical distancing in emerging adults in comparison to older adults and looked at the role of using social media for COVID-19 news and information in this regard. In addition, this study explored the relation between physical distancing and different social media platforms and sources. METHODS Secondary data of a large-scale national longitudinal survey (N = 123,848, 34.% male) between April and November 2020 were used. Participants indicated, ranging for one to eight waves, how often they were successful in keeping 1.5 meters distance on a 7-point Likert scale. Participants between 18 and 24 years old were considered young adults and older participants were identified as older adults. Also, a dummy variable was created to indicate per wave whether participants used social media for COVID-19 news and information. A subset received follow-up questions asking participants to indicate which platforms they have used and what sources of news and information they had seen on social media. All preregistered hypotheses were tested with Linear Mixed-Effects Models and Random Intercept Cross-Lagged Panel Models. RESULTS Emerging adults reported less physical distancing behaviors than older adults (b = -.08, t(86213.83) = -26.79, p < .001). Also, emerging adults were more likely to use social media for COVID-19 news and information (b = 2.48, SE = .11, Wald = 23.66, p = <.001), which mediated the association with physical distancing, but only to a small extend (indirect effect: b = -0.03, 95% CI = [-0.04; -0.02]). Opposed to our hypothesis, the longitudinal Random Intercept Cross-Lagged Panel Model showed no evidence that physical distancing was predicted by social media use of the previous wave. However, we did find evidence that using social media affected subsequent physical distancing behavior. Moreover, additional analyses showed that most social media platforms (i.e., YouTube, Facebook and Instagram) and interpersonal communication showed negative associations with physical distancing while others platforms (i.e. LinkedIn and Twitter) and Governmental messages showed no to a slightly positive associations with physical distancing. CONCLUSIONS In conclusion, we should be vigilant for physical distancing of emerging adults, but this study give no reason the to worry about the role of social media for COVID-19 news and information. However, as some social media platforms and sources showed negative associations, future studies should more carefully look into these factors to better understand the associations between social media use for news and information, and behavioral interventions in times of crisis.

A shift from science to politics : a content analysis of COVID-19 Videos on YouTube (Preprint)

2021 ◽  
Author(s):  
Chyun-Fung Shi ◽  
Matthew C So ◽  
Sophie Stelmach ◽  
Arielle Earn ◽  
David J D Earn ◽  
...  
Keyword(s):  
Public Health ◽  
Social Media ◽  
Content Analysis ◽  
Large Scale ◽  
Control Measures ◽  
Mitigation Measures ◽  
Health Campaigns ◽  
Public Attention ◽  
Conflicting Information ◽  
Social Media Platforms

BACKGROUND The COVID-19 pandemic is the first pandemic where social media platforms relayed information on a large scale, enabling an “infodemic” of conflicting information which undermined the global response to the pandemic. Understanding how the information circulated and evolved on social media platforms is essential for planning future public health campaigns. OBJECTIVE This study investigated what types of themes about COVID-19 were most viewed on YouTube during the first 8 months of the pandemic, and how COVID-19 themes progressed over this period. METHODS We analyzed top-viewed YouTube COVID-19 related videos in English from from December 1, 2019 to August 16, 2020 with an open inductive content analysis. We coded 536 videos associated with 1.1 billion views across the study period. East Asian countries were the first to report the virus, while most of the top-viewed videos in English were from the US. Videos from straight news outlets dominated the top-viewed videos throughout the outbreak, and public health authorities contributed the fewest. Although straight news was the dominant COVID-19 video source with various types of themes, its viewership per video was similar to that for entertainment news and YouTubers after March. RESULTS We found, first, that collective public attention to the COVID-19 pandemic on YouTube peaked around March 2020, before the outbreak peaked, and flattened afterwards despite a spike in worldwide cases. Second, more videos focused on prevention early on, but videos with political themes increased through time. Third, regarding prevention and control measures, masking received much less attention than lockdown and social distancing in the study period. CONCLUSIONS Our study suggests that a transition of focus from science to politics on social media intensified the COVID-19 infodemic and may have weakened mitigation measures during the first waves of the COVID-19 pandemic. It is recommended that authorities should consider co-operating with reputable social media influencers to promote health campaigns and improve health literacy. In addition, given high levels of globalization of social platforms and polarization of users, tailoring communication towards different digital communities is likely to be essential.

scholarly journals Importance-Aware Learning for Neural Headline Editing

2020 ◽  
Vol 34 (05) ◽  
pp. 9282-9289
Author(s):  
Qingyang Wu ◽  
Lei Li ◽  
Hao Zhou ◽  
Ying Zeng ◽  
Zhou Yu
Keyword(s):  
Social Media ◽  
Large Scale ◽  
Network Models ◽  
Language Models ◽  
Neural Network Models ◽  
Generation Task ◽  
Social Media Platforms ◽  
Editing Process ◽  
Different Levels

Many social media news writers are not professionally trained. Therefore, social media platforms have to hire professional editors to adjust amateur headlines to attract more readers. We propose to automate this headline editing process through neural network models to provide more immediate writing support for these social media news writers. To train such a neural headline editing model, we collected a dataset which contains articles with original headlines and professionally edited headlines. However, it is expensive to collect a large number of professionally edited headlines. To solve this low-resource problem, we design an encoder-decoder model which leverages large scale pre-trained language models. We further improve the pre-trained model's quality by introducing a headline generation task as an intermediate task before the headline editing task. Also, we propose Self Importance-Aware (SIA) loss to address the different levels of editing in the dataset by down-weighting the importance of easily classified tokens and sentences. With the help of Pre-training, Adaptation, and SIA, the model learns to generate headlines in the professional editor's style. Experimental results show that our method significantly improves the quality of headline editing comparing against previous methods.

scholarly journals The public accountability of social platforms: lessons from a study on bots and trolls in the Brexit campaign

2018 ◽  
Vol 376 (2128) ◽  
pp. 20180003 ◽  
Author(s):  
Marco Bastos ◽  
Dan Mercea
Keyword(s):  
Social Media ◽  
Large Scale ◽  
Ethical Issues ◽  
Lessons Learned ◽  
Information Warfare ◽  
The Public ◽  
Social Media Platforms ◽  
Application Programming ◽  
Access To Data ◽  
The Uk

In this article, we review our study of 13 493 bot-like Twitter accounts that tweeted during the UK European Union membership referendum debate and disappeared from the platform after the ballot. We discuss the methodological challenges and lessons learned from a study that emerged in a period of increasing weaponization of social media and mounting concerns about information warfare. We address the challenges and shortcomings involved in bot detection, the extent to which disinformation campaigns on social media are effective, valid metrics for user exposure, activation and engagement in the context of disinformation campaigns, unsupervised and supervised posting protocols, along with infrastructure and ethical issues associated with social sciences research based on large-scale social media data. We argue for improving researchers' access to data associated with contentious issues and suggest that social media platforms should offer public application programming interfaces to allow researchers access to content generated on their networks. We conclude with reflections on the relevance of this research agenda to public policy. This article is part of a discussion meeting issue ‘The growing ubiquity of algorithms in society: implications, impacts and innovations'.

scholarly journals Fear of Missing Out as a Predictor of Problematic Social Media Use and Phubbing Behavior among Flemish Adolescents

2018 ◽  
Vol 15 (10) ◽  
pp. 2319 ◽  
Author(s):  
Vittoria Franchina ◽  
Mariek Vanden Abeele ◽  
Antonius van Rooij ◽  
Gianluca Lo Coco ◽  
Lieven De Marez
Keyword(s):  
Social Media ◽  
Large Scale ◽  
Media Use ◽  
Survey Study ◽  
Social Media Use ◽  
Fear Of Missing Out ◽  
Social Media Platforms ◽  
Large Scale Survey ◽  
Problematic Social Media Use ◽  
Use Of Social Media

Fear-of-missing-out (FOMO) refers to feelings of anxiety that arise from the realization that you may be missing out on rewarding experiences that others are having. FOMO can be identified as an intra-personal trait that drives people to stay up to date of what other people are doing, among others on social media platforms. Drawing from the findings of a large-scale survey study among 2663 Flemish teenagers, this study explores the relationships between FOMO, social media use, problematic social media use (PSMU) and phubbing behavior. In line with our expectations, FOMO was a positive predictor of both how frequently teenagers use several social media platforms and of how many platforms they actively use. FOMO was a stronger predictor of the use of social media platforms that are more private (e.g., Facebook, Snapchat) than platforms that are more public in nature (e.g., Twitter, Youtube). FOMO predicted phubbing behavior both directly and indirectly via its relationship with PSMU. These findings support extant research that points towards FOMO as a factor explaining teenagers’ social media use.

scholarly journals Performance Evaluation of Modularity Based Community Detection Algorithms in Large Scale Networks

2014 ◽  
Vol 2014 ◽  
pp. 1-15 ◽  
Author(s):  
Vinícius da Fonseca Vieira ◽  
Carolina Ribeiro Xavier ◽  
Nelson Francisco Favilla Ebecken ◽  
Alexandre Gonçalves Evsukoff
Keyword(s):  
Spectral Method ◽  
Community Detection ◽  
High Performance ◽  
Large Scale ◽  
Fine Tuning ◽  
Detection Algorithms ◽  
Research Areas ◽  
Structure Detection ◽  
Wide Range ◽  
Large Scale Networks

Community structure detection is one of the major research areas of network science and it is particularly useful for large real networks applications. This work presents a deep study of the most discussed algorithms for community detection based on modularity measure: Newman’s spectral method using a fine-tuning stage and the method of Clauset, Newman, and Moore (CNM) with its variants. The computational complexity of the algorithms is analysed for the development of a high performance code to accelerate the execution of these algorithms without compromising the quality of the results, according to the modularity measure. The implemented code allows the generation of partitions with modularity values consistent with the literature and it overcomes 1 million nodes with Newman’s spectral method. The code was applied to a wide range of real networks and the performances of the algorithms are evaluated.

scholarly journals Meditation-induced effects on whole-brain structural and effective connectivity

2021 ◽  
Author(s):  
Eleonora De Filippi ◽  
Anira Escrichs ◽  
Matthieu Gilson ◽  
Marti Sanchez-Fibla ◽  
Estela Camara ◽  
...  
Keyword(s):  
High Performance ◽  
Large Scale ◽  
Effective Connectivity ◽  
Temporal Structure ◽  
Functional Domain ◽  
Anatomical Connectivity ◽  
Whole Brain ◽  
Spatio Temporal ◽  
Large Scale Networks ◽  
And Function

In the past decades, there has been a growing scientific interest in characterizing neural correlates of meditation training. Nonetheless, the mechanisms underlying meditation remain elusive. In the present work, we investigated meditation-related changes in structural and functional connectivities (SC and FC, respectively). For this purpose, we scanned experienced meditators and control (naive) subjects using magnetic resonance imaging (MRI) to acquire structural and functional data during two conditions, resting-state and meditation (focused attention on breathing). In this way, we aimed to characterize and distinguish both short-term and long-term modifications in the brain's structure and function. First, we performed a network-based analysis of anatomical connectivity. Then, to analyze the fMRI data, we calculated whole-brain effective connectivity (EC) estimates, relying on a dynamical network model to replicate BOLD signals' spatio-temporal structure, akin to FC with lagged correlations. We compared the estimated EC, FC, and SC links as features to train classifiers to predict behavioral conditions and group identity. The whole-brain SC analysis revealed strengthened anatomical connectivity across large-scale networks for meditators compared to controls. We found that differences in SC were reflected in the functional domain as well. We demonstrated through a machine-learning approach that EC features were more informative than FC and SC solely. Using EC features we reached high performance for the condition-based classification within each group and moderately high accuracies when comparing the two groups in each condition. Moreover, we showed that the most informative EC links that discriminated between meditators and controls involved the same large-scale networks previously found to have increased anatomical connectivity. Overall, the results of our whole-brain model-based approach revealed a mechanism underlying meditation by providing causal relationships at the structure-function level.

scholarly journals Alternative media for a populist audience? Exploring political and media use predictors of exposure to Breitbart, Sputnik, and Co.

2021 ◽  
Author(s):  
Philipp Müller ◽  
Anne Schulz
Keyword(s):  
Social Media ◽  
Large Scale ◽  
Relative Deprivation ◽  
Media Use ◽  
Alternative Media ◽  
Selective Exposure ◽  
Internet Users ◽  
Social Media Platforms ◽  
New Type ◽  
The Right

Alongside the recent rise of political populism, a new type of alternative media has established in past years that allegedly contribute to the distribution of the populist narrative. Using a large-scale quota survey of German Internet users (n = 1346) we investigate political and media use predictors of exposure to alternative media with an affinity to populism (AMP). Results reveal substantial differences between occasional and frequent AMP users. While both groups heavily use Twitter and Facebook for political information, occasional AMP users exhibit hardly any specific political convictions (except that they feel less personally deprived than non-users). Contrary to that, frequent AMP exposure is related to higher personal relative deprivation, stronger populist attitudes and a higher likelihood to vote for the right-wing populist party AfD. Against this background, frequent AMP use can be interpreted as partisan selective exposure whereas occasional AMP exposure might result from incidental contact via social media platforms. These findings are discussed regarding the role of alternative and social media in the recent populism wave.

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