Big data, small data and everything in between
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This document discusses using both big data and small data analytics to gain theoretical insights. It provides examples of using social network analysis and content analysis of Twitter data to understand communities opposing internet censorship. Additionally, it examines using social media data from foundations and YouTube memes to test theories of diffusion of innovations and cultural spread. The document advocates for using machine learning on larger public data to better map social movements over time while maintaining a focus on connections, content, and context.
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Read the white paper, and find out how small data can lead to breakthrough ideas that transform brands and brand experience.
BUSINESS INTELLIGENCE
El documento presenta una introducción a Business Intelligence. Describe brevemente su historia, definición, características, niveles de realización, elementos, ventajas, software y arquitectura de soporte para la toma de decisiones.
The big story of small data.
Talk at Open University, 11th November 2014.
http://alandix.com/academic/talks/OU-2014-big-story-small-data/
What is Big Data?
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Diffusion of Protest-related Information on Social Media
Daegu Gyeongbuk International Social Network Conference (DISC) 2013 발표자료
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Big data divided (24 march2014)
The document discusses three potential divides that may emerge in big data research: 1) between developed and developing countries, 2) between academic and commercial sector researchers, and 3) between researchers with strong computational skills versus those with less computational skills. It provides examples of methods used in different country/region contexts and notes a critique of big data research around issues like changing definitions of knowledge, misleading claims of objectivity/accuracy, and new digital divides around data access.
Kottler Thesis 2011
The document discusses a study on the role of Twitter in the 2010 Nevada Senate race between Harry Reid and Sharron Angle. It begins by providing background on Twitter and how it has been used in political campaigns. It then discusses different theories about how the internet and new technologies can impact political participation and engagement. Specifically, it examines the instrumental approach which posits that lower communication costs increase participation, and the psychological approach which argues individual motivations and attributes determine online political involvement. The document will analyze tweets from Reid and Angle's campaigns and compare them to mainstream media coverage, in order to understand how Twitter was utilized in this competitive Senate election.
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Talk at Bonn University on general AI and NLP challenges in the context of online discourse analysis. Specific focus on challenges arising from the widespread adoption of neural large language models.
A Dream of Predicting Elections and Trading Stocks using Twitter - Yelena Mej...
This document discusses several studies that aimed to analyze social media data like tweets to track and predict political sentiment and elections. Some key findings include:
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- Analyzing the overlap in audiences of news sources on Twitter correlated with known media bias scores, suggesting social media data could quantify bias.
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- Other research explored detecting politically motivated
What do we get from Twitter - and what not?
This document summarizes popular Twitter research in the social sciences. It discusses exemplary research areas like brand communication, crises/disasters, elections/politics, and popular culture. Popular research visions see Twitter as a new data source with insights into human behavior and thoughts. However, there are limitations and challenges. The document reviews the top cited Twitter publications, which applied methods like interviews, experiments, quantitative analysis, and network/linguistic analysis on datasets ranging from thousands to millions of tweets. Challenges include technical limitations, sampling biases, and different approaches to problems like sentiment analysis.
Predicting opinion leadership on twitter
This document summarizes a study that analyzed Twitter data from the 2012 Wisconsin gubernatorial recall election to predict which users would be influential opinion leaders. The study found that users who were more central in the retweet network, more locally involved in the election issue, and contributed more engaging tweets, were more likely to have their tweets retweeted by others, supporting the hypotheses. Characteristics like centrality and involvement predicted influence, as in traditional models, showing their continued relevance for social media opinion leadership.
Social Media Analytics: Concepts, Models, Methods, & Tools - Ravi Vatrapu
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1. Social media can facilitate information sharing and communication, aiding disaster relief and public health efforts. However, when information is more mediated, people can be anti-social, offline power dynamics are replicated online, and behavior is difficult to measure accurately.
2. While social media aim to be horizontal, in reality prominent offline figures and media elites still hold sway. Measuring public opinion on social media also faces challenges regarding representativeness and reliability.
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Citizen Sensor Data Mining, Social Media Analytics and Applications
Opening talk at Singapore Symposium on Sentiment Analysis (S3A), February 6, 2015, Singapore. http://s3a.sentic.net/#s3a2015
Abstract
With the rapid rise in the popularity of social media, and near ubiquitous mobile access, the sharing of observations and opinions has become common-place. This has given us an unprecedented access to the pulse of a populace and the ability to perform analytics on social data to support a variety of socially intelligent applications -- be it for brand tracking and management, crisis coordination, organizing revolutions or promoting social development in underdeveloped and developing countries.
I will review: 1) understanding and analysis of informal text, esp. microblogs (e.g., issues of cultural entity extraction and role of semantic/background knowledge enhanced techniques), and 2) how we built Twitris, a comprehensive social media analytics (social intelligence) platform.
I will describe the analysis capabilities along three dimensions: spatio-temporal-thematic, people-content-network, and sentiment-emption-intent. I will couple technical insights with identification of computational techniques and real-world examples using live demos of Twitris (http://twitris2.knoesis.org).
Studying Cybercrime: Raising Awareness of Objectivity & Bias
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Kaist 박한우 교수님
This document discusses how social media is used for political mobilization in South Korea. It analyzes the Twitter-based group "Chopae", which aims to expel the conservative newspaper Chosunilbo. The group's organizer sees Twitter as helping to shorten psychological distance with politicians and expand support. A mixed methods study of the group found members shared negative views of government and conglomerates. They formed a collective identity through retweeting information critical of newspapers and the G20 meeting. The group maintained an online hierarchy and used retweets to show solidarity against vested interests.
Disseminating Scientific Papers via Twitter: Practical Insights and Research ...
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About one-fifth of current scientific papers are being shared on Twitter. With 230 million active users and 24 percent of the U.S. online population using the microblogging platform, hopes are high that tweets mentioning scientific articles reflect some type of interest by the general public and might even be able to measure the societal impact of research. However, early studies show that most of the engagement with scientific papers on Twitter takes place among members of academia and thus reflects visibility within the scientific community rather than impact on society. At the same time, some tweets do not involve any human engagement but rather are generated automatically by Twitter bots.
This talk focuses on identifying audiences on Twitter and teaches participants how to collect, analyze, visualize, and interpret diffusion patterns of scientific articles on Twitter. The course provides an overview of Altmetrics research and present the challenges – including methods and first results – of classifying Twitter user groups, with a particular focus on identifying members of the general public and measuring societal impact. The course will provide hands-on exercises and instructions on how to analyze by whom, when, and how scientific papers are shared on Twitter.
Speaker: Stefanie Haustein, Ph.D., Assistant Professor, School of Information Studies, University of OttawaWeller social media as research data_psm15
Presentation at "Preserving Social Media" (#psm15), London, October 27th 2015.
http://dpconline.org/events/details/96-preserving-socialmedia?xref=126%3ASocialMedia15
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Aarhus, June 9, 2015
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Nisbet goucher class
This document discusses several models of science communication and environmental communication. It summarizes research showing that in the 1950s, the public had low scientific literacy but was still generally supportive of science. It also discusses models including the deficit model, Bryan Wynne's model of how the public judges experts, and Kahneman's model of how judgments are context dependent. The document then discusses research testing different frames (environmental, national security, public health) for communicating about climate change in Maine.
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Diffusion of Protest-related Information on Social Media
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R Class: Set up Social Media API
This document provides instructions for setting up APIs in R for Facebook, Twitter, and YouTube data. It explains how to download and install R and RStudio, set up the Facebook and Twitter APIs by obtaining app IDs and secrets, and provides code samples to download the first batch of data from each platform, such as 100 Facebook posts or a specified number of tweets. It also discusses saving the social media data to CSV files.
Say search and sales e-cigar and big data
This document summarizes a study on electronic cigarettes on social media in China. It discusses why the researchers focused on conversations, searching, and selling related to e-cigarettes. It explains that they studied e-cigarettes in China because of China's large smoking population, it being a major manufacturer of e-cigarettes, and the lack of research in this area. The document outlines the key research questions and presents initial findings from analyzing data on the social media platform Sina Weibo, the search engine Baidu, and the e-commerce site Taobao. It discusses the roles of different participants in the conversations and how the conversations evolved over time.
Python Tutorial-Mining imgur images
This tutorial is created for social scientists interested in grabbing data from the image-hosting site, Imgur (imgur.com).
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This document summarizes a study that examines how nonprofits can build social capital through stakeholder engagement on social media, specifically Twitter. The study tests a social (media) capital model to understand how message-based and connection-based investments on Twitter can lead to increased social capital outcomes like network size and influence, and how that social capital then contributes to returns like retweets and reputation. Key findings include evidence that targeting diverse local and nonlocal stakeholders through rich messages predicts acquiring social capital, and that acquired social capital then helps diffuse messages and build online reputation.
Xu talk 3-17-2015
The document summarizes the speaker's research on social media capital from both a network and content perspective. Some key points include:
1) The speaker's dissertation aims to define and quantify social media capital, and develop a predictive model for how investing in social media capital can yield returns.
2) Social media capital is measured based on message-based factors, connection-based factors, network locations, and embedded resources.
3) Research finds that to acquire social media capital and garner word-of-mouth, organizations need to build a large network of diverse, local, and influential stakeholders through their social media activities.
The Networked Cultural Diffusion of Kpop on YouTube
This document summarizes a study that used webometric analysis to examine the network diffusion of the Kpop song "Gangnam Style" on YouTube. The study analyzed over 1,000 comments to understand the actors involved, their networks and interactions, and sentiments expressed. Key findings include that the majority of commenters were young males from North America and Europe. Interactions occurred among a small group of highly engaged users, while most only subscribed. Commenters discussed Korean culture and media. Users from cultures more similar to Korean culture expressed more positive sentiments toward the song. The study provides a webometric approach for understanding cultural diffusion evaluation stages on YouTube.
Network Structures For A Better Twitter Community
This document summarizes a presentation about using different network structures on Twitter to create a better online community. It identifies six types of Twitter networks: divided polarized crowds, unified tight crowds, fragmented brand clusters, clustered community clusters, in-hub and spoke broadcast networks, and out-hub and spoke support networks. The presentation aims to demonstrate how these different network structures can be used to build a healthier Twitter environment.
How Do We Fight Email Phishing? (ICA2015 - San Juan, PR)
A research presentation made in ICA2015:
Email phishing poses a grave security threat to national governments, business operations and average Internet users. The current study presents a model of counter-phishing protective behaviour based on email users' cognitive appraisal of phishing threat and systematic/heuristic processing of email content. The model integrates the theoretical frameworks of protection motivation theory and heuristic-systematic model of information processing. Findings suggest that protective behaviour against email phishing is predicted by a high degree of perceived severity and vulnerability towards cybersecurity risks, a high level of self-efficacy in performing cybersecurity checks and efficacy in responding using cybersecurity tools. The perceived severity, along with the elevated degree of protective behaviour, predicts systematic processing of phishing emails.
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This document summarizes a study that analyzed images related to gun control shared on Twitter to understand what characteristics predict sharing behavior. The researchers developed a coding framework to classify images based on appeals, frames, emotional valence, and intensity. They found that rational appeals and positive valence were most likely to be shared. Attribute frames and positively valenced images had the highest average number of retweets. The study aims to build a predictive model for how image characteristics influence diffusion on social media.
Five steps to get tweets sent by a list of users
In this episode of Python tutorial, we teach you how to gather tweets sent by a list of Twitter users. Download the script at https://drive.google.com/file/d/0Bwwg6GLCW_IPVmNBMUV4bVhUU0U/edit?usp=sharing
Five steps to search and store tweets by keywords
The document provides a 5-step process for searching and storing tweets by keywords using Python and a SQLite database:
1. Install necessary Python libraries like Twython and check that API keys are configured
2. Enter search terms separated by commas
3. Enter Twitter API keys to authorize access to search tweets
4. Configure search parameters like limiting to a specific language or date range
5. Set up a SQLite database to store the tweets returned from the Twitter search
Five Steps to Get Facebook Engagement Indicators
In this tutorial, you will learn how to get:
1. # of likes, shares and comments for all posts on a Facebook page.
2. Content lifecycle: the amount of time for how long the content can drive user attention and engagement.
Curiosity Bits Python Tutorial: Mining Facebook Fan Page - getting posts and ...
In this part of the tutorial, we will teach you how to use Python to get all posts and comments from a given Facebook page.
Curiosity Bits Tutorial: Mining Twitter User Profile on Python V2
This document provides instructions for mining Twitter user profile data using Python. It discusses setting up API keys, installing necessary Python libraries like Twython and SQLite, creating a list of Twitter screennames, setting up a SQLite database to store Twitter data, and analyzing the Python code to extract and store Twitter user profile information from the Twitter API based on the screenname list. The code extracts profile fields like screenname, followers count, location, bio and stores it in the SQLite database. Rate limits from the Twitter API require running the code multiple times to get all user profile data.
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at Integral University, Lucknow, 06.06.2024
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Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
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significant role in maintaining the ecological equilibrium of our planet.Land serves as the foundation for all human activities and provides the necessary materials for
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The utilization of land is impacted by human needs and environmental factors. In countries
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Therefore, human intervention has significantly influenced land use patterns over many
centuries, evolving its structure over time and space. In the present era, these changes have
accelerated due to factors such as agriculture and urbanization. Information regarding land use and
cover is essential for various planning and management tasks related to the Earth's surface,
providing crucial environmental data for scientific, resource management, policy purposes, and
diverse human activities.
Accurate understanding of land use and cover is imperative for the development planning
of any area. Consequently, a wide range of professionals, including earth system scientists, land
and water managers, and urban planners, are interested in obtaining data on land use and cover
changes, conversion trends, and other related patterns. The spatial dimensions of land use and
cover support policymakers and scientists in making well-informed decisions, as alterations in
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Big data, small data and everything in between
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- 3. UNDERSTANDING THE CONTEXT Xu. W.W, & Miao, F. (2015). Networked Creativity on the Censored Web 2.0. Journal of Contemporary Eastern Asia, 14(1), 23-43. “Peeling the onion” to understand the Twitter community against internet censorship 3 Social network analysis
- 4. 4 UNDERSTANDING THE CONTEXT Social network analysis
- 5. Dimension Themes # of Tweets # of Retweets Technological Reporting 172 3702 Sentiment 54 146 Commentary 87 188 Solution 273 7019 Mobilization 20 212 Political Sharing news 130 684 Commentary 121 741 Mobilization -- 69 5 UNDERSTANDING THE CONTEXT Content analysis
- 6. 6 UNDERSTANDING THE CONTEXT Context analysis
- 7. DATA ANALYTICS FOR THEORETICAL INSIGHTS action community information Social connectivity Issue involvement Testing Opinion Leadership Theory on Twitter. • Social connectivity and issue involvement predict retweets • Linking social media analytics to theoretical components Xu, W.W., Sang, Y.M., Blasiola, S., & Park, H.W. (2014). Predicting opinion leaders in Twitter activism networks: The case of the Wisconsin recall election. American Behavioral Scientist, 58(10), 1278-93. 7
- 8. Investment Social Media Capital Return Message-based investment Connection-based investment Network locations Embedded resources Word-of-mouth Recognition DATA ANALYTICS FOR THEORETICAL INSIGHTS Data source: All Twitter-based community foundations in the U.S. 8
- 9. Variety of targeted stakeholders Investment Social Media Capital DATA ANALYTICS FOR THEORETICAL INSIGHTS Return # of targeted local stakeholders # of targeted non-local stakeholders Frequency of stakeholder-targeting Message richness # of tweets the size of acquired stakeholder network the influence of ties with acquired stakeholders the strength of ties with acquired stakeholders the variety of acquired stakeholders Centrality Retweets Recognition Growth in recognition 9
- 10. DATA ANALYTICS FOR THEORETICAL INSIGHTS The evaluation stage in diffusions of (cultural) innovations Semantic network Sentiments Xu, W. W., Park, J. Y., & Park, H. W. (2015). The networked cultural diffusion of Korean Wave. Online Information Review, 39(1). 10
- 11. DATA ANALYTICS FOR THEORETICAL INSIGHTS Xu, W. W., Park, J. Y., Park, Kim, J.Y., & Park, H. W. (in press). Networked cultural diffusion and creation on YouTube: An analysis of YouTube memes. Journal of Broadcasting & Electronic Media. 11 The trial stage in the diffusion of (cultural) innovations Video genres in meme ecosystems
- 12. MOVING FORWARD GO BIGGER AND DELVE DEEPER • Use machine learning for mining public opinion and for tracking social movements: focus on connections, content and context • Provide contextual insights to improve reality-mining algorithm 12
Editor's Notes
- Good afternoon! Good to be back in Daegu. My name is xxxxx. Some of you may know me from previous DISC. I am currently a postdoc at Northeastern U in Boston and work for a big data lab. I am representing Miao Feng who is a researcher affiliated with the HMC at UIC. HMC is a lab dedicated to health research and some key research questions under investigation include how the shifting media landscape change public attitude over smoking. This project you are seeing here is one of such investigation tasked by HMC. If you are interested in HMC, there is a mention of HMC's work in a recent Ted Talk video by Susan Etlinger, titled "What do we do with all this big data?"