238 research outputs found
Modeling the adoption of innovations in the presence of geographic and media influences
While there has been much work examining the affects of social network
structure on innovation adoption, models to date have lacked important features
such as meta-populations reflecting real geography or influence from mass media
forces. In this article, we show these are features crucial to producing more
accurate predictions of a social contagion and technology adoption at the city
level. Using data from the adoption of the popular micro-blogging platform,
Twitter, we present a model of adoption on a network that places friendships in
real geographic space and exposes individuals to mass media influence. We show
that homopholy both amongst individuals with similar propensities to adopt a
technology and geographic location are critical to reproduce features of real
spatiotemporal adoption. Furthermore, we estimate that mass media was
responsible for increasing Twitter's user base two to four fold. To reflect
this strength, we extend traditional contagion models to include an endogenous
mass media agent that responds to those adopting an innovation as well as
influencing agents to adopt themselves
Tracing Public Opinion Propagation and Emotional Evolution Based on Public Emergencies in Social Networks
Social network has become the main communication platform for public emergencies, and it has also made the public opinion influence spread more widely. How to effectively obtain public opinions from it to guide the healthy development of the society is an important issue that the government and other functional departments are concerned about. However, the interaction and evolution mechanism between the subject and the environment in the public opinion propagation is complicated, and the public and media attention and reaction to the incident are closely linked with the progress of the incident disposal. And public mining corpus has some shortcomings in the distribution of emotional classification. Only the timely update of artificial rules and emotional dictionary resources, it can handle new text data well. In fact, from the perspective of public opinion propagation, this paper built the network matrix between Internet users through the forwarding relationship, and used the social network analysis method and the emotion mining analysis technology to study the interaction and evolution mechanism between the subject and the environment in the public opinion propagation, and it studied the role of users in the emotional propagation of social networks. This paper proposed a sentiment analysis method on the micro-blog platform, which expanded the emotional dictionary and took sentence and emoticon and sentence patterns into account, which improved the accuracy of positive and negative classifications and emotional polarity analysis of the micro-blog
Analysis and Extraction of Tempo-Spatial Events in an Efficient Archival CDN with Emphasis on Telegram
This paper presents an efficient archival framework for exploring and
tracking cyberspace large-scale data called Tempo-Spatial Content Delivery
Network (TS-CDN). Social media data streams are renewing in time and spatial
dimensions. Various types of websites and social networks (i.e., channels,
groups, pages, etc.) are considered spatial in cyberspace. Accurate analysis
entails encompassing the bulk of data. In TS-CDN by applying the hash function
on big data an efficient content delivery network is created. Using hash
function rebuffs data redundancy and leads to conclude unique data archive in
large-scale. This framework based on entered query allows for apparent
monitoring and exploring data in tempo-spatial dimension based on TF-IDF score.
Also by conformance from i18n standard, the Unicode problem has been dissolved.
For evaluation of TS-CDN framework, a dataset from Telegram news channels from
March 23, 2020 (1399-01-01), to September 21, 2020 (1399-06-31) on topics
including Coronavirus (COVID-19), vaccine, school reopening, flood, earthquake,
justice shares, petroleum, and quarantine exploited. By applying hash on
Telegram dataset in the mentioned time interval, a significant reduction in
media files such as 39.8% for videos (from 79.5 GB to 47.8 GB), and 10% for
images (from 4 GB to 3.6 GB) occurred. TS-CDN infrastructure in a web-based
approach has been presented as a service-oriented system. Experiments conducted
on enormous time series data, including different spatial dimensions (i.e.,
Khabare Fouri, Khabarhaye Fouri, Akhbare Rouze Iran, and Akhbare Rasmi Telegram
news channels), demonstrate the efficiency and applicability of the implemented
TS-CDN framework
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