[submitted on 31 mar 2020]
title:social
media
mining toolkit (smmt)
download pdf
abstract: there has been a dramatic increase in the popularity of utilizing social
media data for research purposes within the biomedical community. In pubmed
alone, there have been nearly 2,500 publication entries since 2014 that deal
with analyzing social media data from twitter and reddit. However, the vast
majority of those works do not share their code or data for replicating their
studies.
With minimal exceptions, the few that do, place the burden on the
researcher to figure out how to fetch the data, how to best format their data,
and how to create automatic and manual annotations on the acquired data.
There has been a dramatic increase in the popularity of utilizing social media data for research purposes within the biomedical community. In pubmed alone, there have been nearly 2,500 publication entries since 2014 that deal with analyzing social media data from twitter and reddit. However, the vast majority of those works do not share their code or data for replicating their studies. With minimal exceptions, the few that do, place the burden on the researcher to figure out how to fetch the data, how to best format their data, and how to create automatic and manual annotations on the acquired data.
1. Introduction social networks have been studied for many years by social scientists , who are particularly interested in understanding the roles of the people in a social network, how they are connected, and how information spreads among them. Social networks greatly influence how people interact and communicate with one another. In recent years, online social networking has experienced explosive growth, transforming the world wide web into a platform for social interactions. Users share their opinions, photographs, music, and video s on social networking services (snss). Micro-blogging services like twitter have also become an important tool for disseminating realtime information.
The majority of social media text data is freely and publicly available and provides a perfect opportunity to suss out your competition. Find out what your competitors’ customer pain points are, what share of voice you and each of your competitors have, and how it has changed over time.
Information, People, and Technology
Informatics
develops new uses for
information technology,
is interested in how
people transform
technology, and how
technology transforms
us. Bio and social media
informatics lab seeks
various exciting, hard
research problems of
informatics such as
opinion mining, question
answering, topic
detection, and
literature mining in
large scale biomedical
as well as social media
data sets. Looking
for students who
want to work in the
lab!
i am looking
for phd students in the
area of biomedical text
mining or social media
mining.
Qualifications for the
position are as follows:
1) earned a master
degree in computer
science, information
science, or related
disciplines
2) excellent java.
16 Social Media Mining
Posting images on social media has been a common activity in the mobile internet era. Many research has been conducted on social media text mining, but few studies focus on social media image mining. We believe that image mining can provide insights about society from another perspective, so we developed a geoai-based system to collect, store, and analyze millions of social media images (twitter) in real-time. This platform is designed to be extensible and flexible so that various image analysis models and apis( e. G. , a trained cnn) can be easily plugged. Currently, we have added a flooding photo detector, an object detector, and a face detector to the platform.
Eman s. Al-sheikh & mozaherul hoque abul hasanat, 2018. " social media mining for assessing brand popularity ," international journal of data warehousing and mining (ijdwm) , igi global, vol. 14(1), pages 40-59, january. Handle: repec:igg:jdwm00:v:14:y:2018:i:1:p:40-59 cited by: more about this item access and download statistics.
In this review, we are taking 3 data mining technique to analyses the twitter sentiment analysis. The entire chosen algorithm is robust. Some algorithms are dependent upon one another for example dbscan and fastdbscan. Data has played a vital role in this era, which helps in analyzing the mode of users. Data can be extracted from anywhere like social media, some api and so on. These data sizes are not normal and can can't be easily analyzed.
The short answer is yes. Social media has become the quintessential networking tool for people, businesses and organizations alike. It has the power to connect brands to their consumers, inform audiences on current topics and trends, and engage audiences to interact and discuss opinions. Whether we like it, use it, or just don’t understand it, social media can be useful for everyone—even mining companies. • related content: anglo american: digging smarter, not harder mining companies have notoriously shied away from public exposure pertaining to mining activities. However, like it or not, much of the information that circulates about the mining industry’s environmental, social and governance (esg) performance takes the form of pictures, tweets and infographics.
Social Media Data Mining Techniques
Dining is an essential part of human life. In order to pursue a healthier self, more and more people enjoy homemade cuisines. Consequently, the amount of recipe websites has increased significantly. These online recipes represent different cultures and cooking methods from various regions, and provide important indications on nutritional content. In recent years, the development of data science made data mining a popular research area. However, only a few researches in taiwan have applied data mining in the studies of recipes and nutrients. Therefore, this work aims at utilizing machine learning models to discover health-related insights from recipes on social media.
Social Media Data Mining – What It Is & How It Works
Nowadays, social networking is popular. As such, numerous social networking sites (e. G. , facebook, youtube, instagram) are generating very large volumes of social data rapidly. Valuable knowledge and information is embedded into these big social data. As the social network can be very sparse, it is awaiting to be (a) compressed via social network data compression and (b) analyzed and mined via social network analysis and mining. We present in this paper a solution for compressing and mining social networks. It gives an interpretable compressed representation of sparse social network, and discovers interesting patterns from the social network.
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