From the Twitter Stream to your Stats Screen:
|
|
|
- Bernard Lyons
- 5 years ago
- Views:
Transcription
1 From the Twitter Stream to your Stats Screen: Towards Working with Social Media Data for Official Statistics H. Andrew International Conference and Global Working Group meeting on Big Data for Official Statistics 29 October, 2014, Beijing, China...shedding light on psychosocial phenomena through big language analysis.
2 Thank You United Nations Statistics Division (UNSD) National Bureau of Statistics of China (NBS)
3 Social Media
4 Social Media 300mil. tweets/day
5 Social Media 300mil. tweets/day 4bil. messages/day
6 Social Media 300mil. tweets/day 4bil. messages/day 100mil. (Sina) weibos/day
7 Social Media 300mil. tweets/day 4bil. messages/day 100mil. (Sina) weibos/day BIGGER DATA
8 Social Media 300mil. tweets/day 4bil. messages/day 100mil. (Sina) weibos/day
9 Social Media PEOPLE: 300mil. tweets/day 150mil. 4bil. messages/day 1bil. 100mil. (Sina) weibos/day 75mil. (2014) (2014) (2014)
10 Social Media PEOPLE: 300mil. tweets/day 150mil. 4bil. messages/day 1bil. 100mil. (Sina) weibos/day 75mil. (2014) (2014) (2014) Largest dataset(s) of everyday human behavior and conerns.
11 Social Media Applications Largest dataset(s) of everyday human behavior and conerns.
12 Social Media 1. Measurement Applications Largest dataset(s) of everyday human behavior and conerns.
13 Social Media 1. Measurement To what extent can we replace traditional survey-based methods? Applications Largest dataset(s) of everyday human behavior and conerns.
14 Measurement: Personality
15 Measurement: Personality
16 Measurement: Personality
17 Social Media 1. Measurement To what extent can we replace traditional survey-based methods? Applications Largest dataset(s) of everyday human behavior and conerns.
18 Social Media 1. Measurement To what extent can we replace traditional survey-based methods? 2. Data-driven discovery Applications Largest dataset(s) of everyday human behavior and conerns.
19 Social Media 1. Measurement To what extent can we replace traditional survey-based methods? 2. Data-driven discovery Can we discovery new links with outcomes? What is driving a trend? Applications Largest dataset(s) of everyday human behavior and conerns.
20 Data-driven Social Science: Extraversion sociable, assertive, active, energetic, talkative, outgoing Schwartz, H. A., Eichstaedt, J. C., Kern, M. L., Dziurzynski, L., Ramones, S. M., Agrawal, M., Shah, A., Kosinski, M., Stillwell, D., Seligman, M. E. P., & Ungar, L. H. (2013). Personality, Gender, and Age in the Language of Social Media: The OpenVocabulary Approach. In PLOS ONE 8(9).
21 Data-driven Social Science: Introversion Schwartz, H. A., Eichstaedt, J. C., Kern, M. L., Dziurzynski, L., Ramones, S. M., Agrawal, M., Shah, A., Kosinski, M., Stillwell, D., Seligman, M. E. P., & Ungar, L. H. (2013). Personality, Gender, and Age in the Language of Social Media: The OpenVocabulary Approach. In PLOS ONE 8(9).
22 Data-Driven Social Science: Neuroticism moody, anxious, fearful, worry-prone, depressive Explicit Language Warning
23 Data-Driven Social Science: Neuroticism moody, anxious, fearful, worry-prone, depressive
24 Data-Driven Social Science: Neuroticism moody, anxious, fearful, worry-prone, depressive
25 Data-Driven Social Science: Neuroticism
26 Data-Driven Social Science: Neuroticism
27 Social Media 1. Measurement To what extent can we replace traditional survey-based methods? 2. Data-driven discovery Can we discovery new links with outcomes? What is driving a trend?
28 Social Media 1. Measurement To what extent can we replace traditional survey-based methods? 2. Data-driven discovery Can we discovery new links with outcomes? What is driving a trend? Big Data? Traditional Official Statistics
29 Social Media 1. Measurement To what extent can we replace traditional survey-based methods? 2. Data-driven discovery Can we discovery new links with outcomes? What is driving a trend? Traditional Big Official Data Statistics
30 Overview Introduction Background on Social Media Data Examples Challenges Summary
31 Overview Introduction Background on Social Media Data Sources Types Acquisition Analysis Methodology Examples Challenges Summary
32 Social Media Sources microblogging Twitter Weibo social interaction Facebook Renren messaging Text Messages SnapChat WeChat mostly public somewhat private private
33 Social Media Sources microblogging Twitter Weibo social interaction Facebook Renren messaging Text Messages SnapChat WeChat mostly public somewhat private private
34 Social Media Sources microblogging Twitter Weibo social interaction Facebook Renren messaging Text Messages SnapChat WeChat mostly public big somewhat private bigger private biggest
35 Social Media Sources microblogging Twitter Weibo social interaction Facebook Renren mostly public somewhat private big bigger Other social media Instagram YouTube Yelp Pinterest Tumblr Reddit messaging Text Messages SnapChat WeChat private biggest
36 Social Media Sources microblogging Twitter Weibo social interaction Facebook Renren mostly public big somewhat private bigger Other social media Instagram YouTube Yelp Pinterest Tumblr Reddit messaging Text Messages SnapChat WeChat private biggest Search Google Yahoo Baidu Bing
37 Social Media Data Types: Text!
38 Social Media Data Types: Text!
39 Social Media Data Types: Text!
40 Social Media Data Types: Text!
41 Social Media Data Types: Text!
42 Social Media Data Types: Text!
43 Social Media Data Types: Levels of Analysis
44 Social Media Data Types:
45 Acquiring Social Media Twitter Application Programming Interfaces (APIs) random stream (1% daily = ~2 to 3.5m) filter stream (1%; not random sample) search API (180 queries per 15 minutes)
46 Acquiring Social Media Twitter Application Programming Interfaces (APIs) random stream (1% daily = ~2 to 3.5m) filter stream (1%; not random sample) search API (180 queries per 15 minutes) More data provided by third parties (Datasift, Gnip,...)
47 Acquiring Social Media JSON encoding { "coordinates": None, "created_at": "Wed Jan 29 22:58: ", "favorite_count": 19, "favorited": False, "geo": None, "id": , "lang": "en", "place": None, "retweet_count": 14, "retweeted": False, "text": "Wow, where did January go? Was I in Tulsa or Yemen? Or Vermont?",... }
48 Acquiring Social Media Facebook o Graph API o Limited public data o Consent participants to share private data through Facebook App.
49 Analysis / Methodology
50 Analysis / Methodology Features words and phrases: 1 to 3 word sequences more likely to occur together than chance. Words identified from text via social-media aware tokenization. usually restricted to those used more than a few times e.g. 'day', 'the beautiful day', 'Mexico City', etc...
51 Analysis / Methodology Features words and phrases: 1 to 3 word sequences more likely to occur together than chance. Words identified from text via social-media aware tokenization. usually restricted to those used more than a few times e.g. 'day', 'the beautiful day', 'Mexico City', etc... topics: Clusters of semantically-related words found via latent Dirichlet allocation e.g.
52 Method:Data-driven language analysis Features words and phrases: 1 to 3 word sequences more likely to occur together than chance. topics: Clusters of semantically-related words found via latent Dirichlet allocation lexica: Manually-created clusters of words e.g. positive emotion: happy, joyous, like, etc negative emotion: sad, hate, terrible, etc
53 Analysis / Methodology open-vocabulary : Not restricted to predefined lists of features.
54 Analysis / Methodology Example: Sentiment Analysis Thumbs up... (Pang and Lee, 2004) + / - Emotion from LIWC (Pennebaker et al., 2001) NRC Canada (Mohammad et al., 2013
55 Analysis / Methodology All require validation in new domain. (e.g., new platform, time-frame, or level of analysis)
56 Analysis / Methodology Prediction How to fit a single model on lots of language variables? (e.g. 25,000 words and phrases) Methods from Machine Learning: discrete outcomes: support vector machines (SVM) continuous outcomes: ridge regression
57 Analysis / Methodology Prediction Issues with words as variables: sparseness: most words do not occur very often high co-variance: e.g. people that say soccer often are also more likely to say goal
58 Analysis / Methodology Prediction Issues with words as variables: sparseness: most words do not occur very often high co-variance: e.g. people that say statistics often are also more likely to say variable Solutions: L1 penalized fit (lasso regression) Use principal components analysis before fit
59 Analysis / Methodology
60 Some Available Resources MALLET: Machine Learning Language Toolkit Good for topic modeling GUI: Lightside: Point and Click Machine Learning WWBP Resources wwbp.org/data.html Coming this January: LexHub: Language Analysis X social science to get on list: hansens@seas.upenn.edu
61 Overview Introduction Background on Social Media Data Sources Types Acquisition Analysis Methodology Examples Challenges Summary
62 Overview Introduction Background on Social Media Data Examples Heart Disease Mortality HIV Prevalence Life Satisfaction Flu Tracking Challenges Summary
63 Example: Community Heart Disease Mortality Eichstaedt, Schwartz, Park, Kern, Ungar, Seligman. (2014; in press)
64 Example: Community Heart Disease Mortality Twitter Dataset Studied: 10% of tweets from June 2009 to March 2010 (826 million tweets) United States CDC data: Atherosclerotic Heart Disease Mortality
65 Example: Community Heart Disease Mortality
66 Example: Community Heart Disease Mortality
67 Example: Community Heart Disease Mortality * **
68 Example: Community Heart Disease Mortality * **
69 Example: Community Heart Disease Mortality * **
70 Example: Community Heart Disease Mortality * **
71 Example: Community Heart Disease Mortality * **
72 Example: Community Heart Disease Mortality * **
73 Language positively correlated with US-county-level Heart Disease
74 Language negatively correlated with US-county-level Heart Disease
75 Example: County Life Satisfaction In collaboration with Molly Ireland and Dolores Albaraccin
76 Example: County Life Satisfaction education level, income, demographics, ethnicity Twitter
77 Example: County Life Satisfaction
78 Example: County HIV Prevalence In collaboration with Molly Ireland and Dolores Albaraccin
79 Example: County HIV Prevalence
80 Example: County HIV Prevalence HIV prevalence is higher in counties with less future tense in... all 1375 qualifying counties (Beta = -0.48, p <.001) top 200 most populated counties (Beta = -0.27, p <.001)
81 Example: Flu Trends
82 Google Flu Trends
83 Health Tweets (Mark Dredze and Michael Paul; Johns Hopkins University) narrows in on health-related tweets
84 Overview Introduction Background on Social Media Data Examples Heart Disease Mortality HIV Prevalence Life Satisfaction Flu Tracking Challenges Summary
85 Overview Introduction Background on Social Media Data Examples Challenges Summary
86 Challenges Ethical / Privacy Technical Methodological
87 Challenges
88 Challenges Ethical / Privacy Public Awareness / Participant Consent Technical Methodological
89 Challenges Ethical / Privacy Public Awareness / Participant Consent Technical Data Storage and Analysis Infrastructure Evolving APIs Methodological
90 Challenges Ethical / Privacy Public Awareness / Participant Consent Technical Data Storage and Analysis Infrastructure Evolving APIs Methodological Word meaning / domains Correlation versus Causation Sample Bias Self-presentation Bias
91 Issues attributed to missclassification Facebook status update.
92 Challenges Ethical / Privacy Public Awareness / Participant Consent Technical Data Storage and Analysis Infrastructure Evolving APIs Methodological Word meaning / domains Correlation versus Causation Sample Bias Self-presentation Bias
93
94
95
96
97
98
99
100 Representative Sample? Alternaitve: Post-stratification Demographics are one of the most accurately predicted from language gender 92% accuracy age 0.86 correlation
101 Challenges Ethical / Privacy Public Awareness / Participant Consent Technical Data Storage and Analysis Infrastructure Evolving APIs Methodological Word meaning / domains Correlation versus Causation Sample Bias Self-presentation Bias
102 Challenges Ethical / Privacy Public Awareness / Participant Consent Technical Data Storage and Analysis Infrastructure Evolving APIs Methodological Word meaning / domains Correlation versus Causation Sample Bias Self-presentation Bias validate
103
104 Thank You! Questions? Big Data Traditional Official Statistics wwbp.org
105 Thank You! Questions? Big Data for Official Statistics wwbp.org
106 APPENDIX
107 Method: County-Mapping 94% accurate map to human-judged intended city, state pair.
108 Distributed Computing approximately 1 billion tweets Too much for single computer system Utilize map-reduce in a Hadoop style cluster: image:
109 Well-Being and Policy => Life Satisfaction (across domains)
110 What topics matter for all counties (that we have data for) in the United States? Evidence for moderation A moderator alters the strength or direction of a relationship Question of external validity how universal is the effect? Daivd Kenny Moderator Variables: Introduction, What topics matter for the poorest 25% of counties in
111 Individual Well-Being satisfaction with life
112 Individual Well-Being: message to user-level message and user-level
113
114
115
116 Representative Sample? (i.e. implicitly maps unrepresentative sample to representative) Fit unrepresentative sample to representative sample results In the end we are validating against representative data.
117 Individual Traits in Facebook MyPersonality Dataset Facebook application to take Big-5 personality survey. Approximately 75,000 users of the app: o shared their status updates for research o wrote at least 1,000 words o share their age and gender
118 Community Well-Being Through Twitter
119 Community Well-Being through Twitter Twitter > 150 million active monthly users > 350 million messages a day often list a location or geo-coordinates
120
121 You Are What You Tweet status update! status status update! update! status update! status update! tweet! tweet! tweet! tweet! language analysis? tweet! Outcomes prediction (measuremen t) insights
122 Example JSON - Tweet { "coordinates": None, "created_at": "created_at": "Wed "Wed Jan Jan :58:50 22:58: ", 2014", "favorite_count": 19, "favorited": False, "geo": None, "id": , "lang": "en", "place": None, "retweet_count": 14, "retweeted": False, "text": "Wow, where did January go? Was I in Tulsa or Yemen? Or Vermont?",... }
123 Twitter APIs REST APIs REST APIs o Twitter App building (e.g. smartphone apps) o Search API o Streaming APIs o o Firehose o public random sample o user and site streams Twitter App building (e.g. smartphone apps) Search API Streaming APIs o o o Firehose public random sample user and site streams
124 Sample Stream Sample Stream 1 % of all public tweets real time useful for representative language sample o o less than 40% of tweets are in English can be useful for frequencies of terms looked at
125 Search Stream Search API Specific to what you re looking for same content as the web search parameters include o o o o o Recent vs Top tweets Geolocalization Language filter (Twitter s algorithm is best effort ) time ranges (limited) more:
126 Community Heart Disease through Twitter Method: Prediction Lasso, L1 penalized, regression Controls: demographics: age, gender, ethnicity socio-economic status: income, education Schwartz, H. A., Eichstaedt, J. C., Kern, M. L., Dziurzynski, L., Lucas, R. E., Agrawal, M., Park, G. J., Lakshmikanth, S. K., Jha, S., Seligman, M. E. P., & Ungar, L. H. (2013). Characterizing Geographic Variation in Well-Being using Tweets. In Proceedings of the Seventh International AAAI Conference on Weblogs and Social Media (ICWSM). Boston, MA.
127 Search Stream Search API Specific to what you re looking for same content as the web search parameters include o o o o o Recent vs Top tweets Geolocalization Language filter (Twitter s algorithm is best effort ) time ranges (limited) more:
128 Who has access to APIs? Twitter uses OAuth2 for authentication Not a username, password authentication Need a Twitter App (and a Twitter account) o o o Anyone can create a blank app Go to Generate API key, API secret, access token & access secret on this page:
129 What s in a Tweet JSON text of the tweet Find a complete list of fields at: & unique Twitter id created date & time replies: o user id & tweet id of tweet replied to retweets: o Tweet JSON of the original tweet favorited & retweeted counts entities o expanded links, hashtags, media & user mentions user info: o o o o unique Twitter id screen name, handle, location, description nb tweets, favourites, followers profile picture & background information!! Some fields are optional!! Example Tweet JSON:
130 Limitations of Twitter API Sample Stream: only 1 % of all tweets terms that aren t frequent enough might not even appear in your dataset Search: 180 queries limit in every 15 minute window each search query can only contain 10 terms
131 Facebook API APIs Free Free GraphAPIs API o o o API o Chat Graph FQL API API Third APIs o party Chat API o Public Feed API o o Keywords Insights API FQL API That s where the data is Third party APIs o o Public Feed API Keywords Insights API
132 Graph API Every data point is a node in a graph John John John John John s friends Likes & Comments John s posts
133 Graph API Every data point is a node in a graph John John John John John s friends Likes & Comments John s posts
134 What did we learn? API = Application Programming Interface Easier for huge amounts of data Twitter has multiple APIs So does Facebook How to use the Graph API to post/delete a status You might want to ask your programmer for help
135
136 Individual Traits in Facebook
137 Individual Traits in Facebook MyPersonality Dataset Facebook application to take Big-5 personality survey. Approximately 75,000 users of the app: o shared their status updates for research o wrote at least 1,000 words o share their age and gender
138 Individual Traits in Facebook: Female Gender
139 Individual Traits in Facebook: Male Gender
A Brief Overview of Facebook and NLP. Presented by Brian Groenke and Nabil Wadih
A Brief Overview of Facebook and NLP Presented by Brian Groenke and Nabil Wadih Overview Brief History of Facebook Usage and Growth Relevant NLP Research Facebook APIs Facebook Sentiment: Reactions and
Online survey with children aged June 2017
Online survey with children aged 12-15 June 2017 Establish with parent Whether a parent or guardian of a child in the household Age and gender of child Region/ nation Household socio-economic group Urbanity
On the challenges of cross-national comparative research of NLP
CLARIN-PLUS workshop Creation and Use of Social Media Resources Kaunas Hotel, Lithuania, 18-19 May 2017 On the challenges of cross-national comparative research of NLP Jiyoung Ydun Kim PhD student Anja
Exploring the New Trends of Chinese Tourists in Switzerland
Exploring the New Trends of Chinese Tourists in Switzerland Zhan Liu, HES-SO Valais-Wallis Anne Le Calvé, HES-SO Valais-Wallis Nicole Glassey Balet, HES-SO Valais-Wallis Address of corresponding author:
The Podcast Consumer. May 2015
The Podcast Consumer May 2015 Methodology Overview In January/February 2015, Edison Research conducted a national telephone survey of 2002 people aged 12 and older, using random digit dialing techniques.
Understanding the city to make it smart
Understanding the city to make it smart Roberta De Michele and Marco Furini Communication and Economics Department Universty of Modena and Reggio Emilia, Reggio Emilia, 42121, Italy, marco.furini@unimore.it
Ethical, Epistemological, Methodological, Social and Other
Ethical, Epistemological, Methodological, Social and Other Issues in Web/Social Media Mining Marko M. Skoric Department of Communication PhD Student Workshop Web Mining for Communication Research April
The Relationship Between Self Construal, Media Use and Dreams: A Cross Cultural Study (Taiwan and Canada)
The Relationship Between Self Construal, Media Use and Dreams: A Cross Cultural Study (Taiwan and Canada) Jayne Gackenbach*, Ming-Ni Lee, Sarah Gahr* and Yue Yu *MacEwan University; National Dong Hwa University,
Sentiment Analysis of User-Generated Contents for Pharmaceutical Product Safety
Sentiment Analysis of User-Generated Contents for Pharmaceutical Product Safety Haruna Isah, Daniel Neagu and Paul Trundle Artificial Intelligence Research Group University of Bradford, UK Haruna Isah
Evangelical Christians on The Infinite Dial. #infinitedial
Evangelical Christians on The Infinite Dial #infinitedial Methodology Overview In January/February 2014, Edison Research conducted a national telephone survey of 2,023 people aged 12 and older, using random
the role of mobile computing in daily life
the role of mobile computing in daily life Alcatel-Lucent Bell Labs September 2010 Paul Pangaro, Ph.D. CTO, CyberneticLifestyles.com New York City paul@cyberneticlifestyles.com 1 mobile devices human needs
The Infinite Dial 2014
The Infinite Dial 2014 A Look at #infinitedial Methodology Overview In January/February 2014, Edison Research conducted a national telephone survey of 2,023 people aged 12 and older, using random digit
LAB #5: GETTING STARTED WITH SOCIAL MEDIA. ERIKA DEBLASI, CRC President
LAB #5: GETTING STARTED WITH SOCIAL MEDIA ERIKA DEBLASI, CRC President LAB GOALS SET UP (or dust off) your business social media accounts. UNDERSTAND how to post on each KNOW the basic Dos & Don ts for
Findings of a User Study of Automatically Generated Personas
Findings of a User Study of Automatically Generated Personas Joni Salminen Qatar Computing Research Institute, Hamad Bin Khalifa University and Turku School of Economics jsalminen@hbku.edu.qa Soon-Gyo
How to Take Your Offline Business Online An Introduction to Online Visibility. By Vladas Krivickas
How to Take Your Offline Business Online An Introduction to Online Visibility By Vladas Krivickas Securing Your Brand Online Your #1 Job on the Internet Whoever you are and whatever you do, your number
Infographic: Google Search Prevalence by State
Chitika Insights Infographic: Google Search Prevalence by State August 13, 2013 A publication of 1 Introduction While Google has long been the most-used search engine domestically, Chitika Insights latest
WHITE PAPER. NLP TOOL (Natural Language Processing) User Case: isocialcube (Social Networks Campaign Management)
WHITE PAPER NLP TOOL (Natural Language Processing) User Case: isocialcube (Social Networks Campaign Management) www.aynitech.com What does the Customer need? isocialcube s (ISC) helps companies manage
A whitepaper by. Consumption of Communication
A whitepaper by Consumption of Communication Consumption of Communication KEY TAKEAWAYS Section 1: Consumption of communication platforms and apps 92% of people use a modern technological platform or app
THE PODCAST CONSUMER AUSTRALIA 2017
THE Study Overview The Infinite Dial is the longest-running survey of digital media consumer behavior in America This inaugural Infinite Dial Australia report mirrors the Infinite Dial reports which have
MSc(CompSc) List of courses offered in
Office of the MSc Programme in Computer Science Department of Computer Science The University of Hong Kong Pokfulam Road, Hong Kong. Tel: (+852) 3917 1828 Fax: (+852) 2547 4442 Email: msccs@cs.hku.hk (The
Instagram for Beginners. By Leanne O Sullivan, the Summit School
Instagram for Beginners By Leanne O Sullivan, the Summit School Where to start with Instagram Download the APP Instagram for those very very new is ALL mobile, which can seem annoying at first when you
The University of Sheffield Research Ethics Policy Note no. 14 RESEARCH INVOLVING SOCIAL MEDIA DATA 1. BACKGROUND
The University of Sheffield Research Ethics Policy te no. 14 RESEARCH INVOLVING SOCIAL MEDIA DATA 1. BACKGROUND Social media are communication tools that allow users to share information and communicate
Twitter Tips for small business by Trudy Thompson
Twitter Tips for small business by Trudy Thompson Apart from spending my time tweeting, I also do this Bricks and Bread Sustainable Living Centre A business incubator hub for small businesses collaborating
Latest trends in sentiment analysis - A survey
Latest trends in sentiment analysis - A survey Anju Rose G Punneliparambil PG Scholar Department of Computer Science & Engineering Govt. Engineering College, Thrissur, India anjurose.ar@gmail.com Abstract
Twitter Quick Start Guide
Twitter Quick Start Guide Twitter Quick Start Guide 1 So, you want to expand your business by using Twitter, but you re not sure where to start? Well, the first thing is that you need to sign up for a
Title of your Blog. Your Name
Title of your Blog Your Name Social Links Blog: clickable url here Twitter: clickable url here Facebook: clickable url here Your other social link (i.e. Pinterest, Instagram) (for Instagram use a link
The Infinite Dial 2014
The Infinite Dial 2014 A Look at #infinitedial Methodology Overview In January/February 2014, Edison Research conducted a national telephone survey of 2,023 people aged 12 and older, using random digit
Truthy: Enabling the Study of Online Social Networks
arxiv:1212.4565v2 [cs.si] 20 Dec 2012 Karissa McKelvey Filippo Menczer Center for Complex Networks and Systems Research Indiana University Bloomington, IN, USA Truthy: Enabling the Study of Online Social
State of Podcasting: 2018 A white paper from Authentic, A Podtrac Company
Is Podcasting Ready for Your Brand? State of Podcasting: 2018 A white paper from Authentic, A Podtrac Company Last update: May 2018 https://docs.google.com/document/d/15shv7ast-e78wgaelpl8hympfg2hto03vsy5_4bztfg/edit#heading=h.2lv52knphi88
THE INFINITE DIAL. #infinitedial #INFINITEDIAL
THE INFINITE DIAL 2016 2016 #infinitedial #INFINITEDIAL Study Overview The Infinite Dial is the longest-running survey of digital media consumer behavior in America. The annual reports in this series have
The Infinite Dial 2014
The Infinite Dial 2014 A Look at #infinitedial Methodology Overview In January/February 2014, Edison Research conducted a national telephone survey of 2,023 people aged 12 and older, using random digit
League of Legends: Dynamic Team Builder
League of Legends: Dynamic Team Builder Blake Reed Overview The project that I will be working on is a League of Legends companion application which provides a user data about different aspects of the
Social Network Behaviours to Explain the Spread of Online Game
Social Network Behaviours to Explain the Spread of Online Game 91 Marilou O. Espina orcid.org/0000-0002-4727-6798 ms0940067@yahoo.com Bukidnon State University Jovelin M. Lapates orcid.org/0000-0002-4233-4143
MEASURING PRIVACY RISK IN ONLINE SOCIAL NETWORKS. Justin Becker, Hao Chen UC Davis May 2009
MEASURING PRIVACY RISK IN ONLINE SOCIAL NETWORKS Justin Becker, Hao Chen UC Davis May 2009 1 Motivating example College admission Kaplan surveyed 320 admissions offices in 2008 1 in 10 admissions officers
Privacy, Technology and Economics in the 5G Environment
Privacy, Technology and Economics in the 5G Environment S A M A N T K H A J U R I A A S S I S T P R O F E S S O R, C M I K N U D E R I K S K O U B Y P R O F E S S O R, D I R E C T O R C M I S K O U B Y
SOCIAL MEDIA SUPERHERO the workbook
SOCIAL MEDIA SUPERHERO the workbook 1. Why did you join Social Media Superhero? 2. What are you hoping to learn or get out of this course? 3. What does HAC stand for? 4. Explain the importance of each
2016 Smart Cities Survey Summary Report of Survey Results
Introduction 2016 Smart Cities Survey Summary Report of Survey Results In 2016, the International City/County Management Association (ICMA) conducted a survey in partnership with the Smart Cities Council
Social Network Analysis in HCI
Social Network Analysis in HCI Derek L. Hansen and Marc A. Smith Marigold Bays-Muchmore (baysmuc2) Hang Cui (hangcui2) Contents Introduction ---------------- What is Social Network Analysis? How does it
Predicting Video Game Popularity With Tweets
Predicting Video Game Popularity With Tweets Casey Cabrales (caseycab), Helen Fang (hfang9) December 10,2015 Task Definition Given a set of Twitter tweets from a given day, we want to determine the peak
Large Scale Topic Detection using Node-Cut Partitioning on Dense Weighted-Graphs
Large Scale Topic Detection using Node-Cut Partitioning on Dense Weighted-Graphs Kambiz Ghoorchian Šarūnas Girdzijauskas ghoorian@kth.se 22.06.206 Motivation Solution Results Conclusion 2 What is a Topic
THE INFINITE DIAL 2016
THE INFINITE DIAL 2016 2016 #infinitedial #INFINITEDIAL Study Overview The Infinite Dial is the longest-running survey of digital media consumer behavior in America. The annual reports in this series have
Understanding User Privacy in Internet of Things Environments IEEE WORLD FORUM ON INTERNET OF THINGS / 30
Understanding User Privacy in Internet of Things Environments HOSUB LEE AND ALFRED KOBSA DONALD BREN SCHOOL OF INFORMATION AND COMPUTER SCIENCES UNIVERSITY OF CALIFORNIA, IRVINE 2016-12-13 IEEE WORLD FORUM
Digitisation A Quantitative and Qualitative Market Research Elicitation
www.pwc.de Digitisation A Quantitative and Qualitative Market Research Elicitation Examining German digitisation needs, fears and expectations 1. Introduction Digitisation a topic that has been prominent
THE INFINITE DIAL #infinitedial #INFINITEDIAL. A Look at African-Americans & Hispanics
THE INFINITE DIAL 2016 2016 #infinitedial #INFINITEDIAL A Look at African-Americans & Hispanics Study Overview The Infinite Dial is the longest-running survey of digital media consumer behavior in America.
Build the Easiest Backlinks First. Build Internal Links
Build the Easiest Backlinks First Most link building tactics fail because of lack of effort on the part of the link builder. Failing to generate momentum at the onset of the campaign is the kiss of death.
Predicting Content Virality in Social Cascade
Predicting Content Virality in Social Cascade Ming Cheung, James She, Lei Cao HKUST-NIE Social Media Lab Department of Electronic and Computer Engineering Hong Kong University of Science and Technology,
AN EFFICIENT METHOD FOR FRIEND RECOMMENDATION ON SOCIAL NETWORKS
AN EFFICIENT METHOD FOR FRIEND RECOMMENDATION ON SOCIAL NETWORKS Pooja N. Dharmale 1, P. L. Ramteke 2 1 CSIT, HVPM s College of Engineering & Technology, SGB Amravati University, Maharastra, INDIA dharmalepooja@gmail.com
WORKSHOP. Sara Bauer Ma, MSc. Computational Linguistics or "How your last tweet will be used against you" September 28, 2018
Ma, MSc WORKSHOP Computational Linguistics or "How your last tweet will be used against you" September 28, 2018 Contents Introduction Social Media Background Practice and Examples Summary 1/20 About Me
Story Excitement Social Compete Escapism Challenge Creation Achieve Curiosity Fantasy Hilgard et al. (2013) Rigby et al. (2006) Bartle (1996) Sherry e
Story Excitement Social Compete Escapism Challenge Creation Achieve Curiosity Fantasy Hilgard et al. (2013) Rigby et al. (2006) Bartle (1996) Sherry et al. (2006) LeBlanc (2004) Steinkuehler (2005) Yee
MODULE 4 CREATING SOCIAL MEDIA CONTENT
MODULE 4 CREATING SOCIAL MEDIA CONTENT Introduction Hello, this is Stefan, and welcome to Module 4, Creating YouTube Videos. Types of Social Media Content There are many different types of social media
KELLER REALTY WILLIAMS. Getting Started on Twitter. Brought to you by Keller Williams Realty
KELLER WILLIAMS REALTY 101 Getting Started on Twitter Brought to you by Keller Williams Realty What are you doing? This simple question has been the basis for the phenomenon known as Twitter. A Website
GAME AUDIENCE DASHBOARD MAIN FEATURES
GAME AUDIENCE DASHBOARD MAIN FEATURES WE COMBINED PSYCHOMETRIC METHODS AND A WEB APP TO COLLECT MOTIVATION DATA FROM OVER 300,000 GAMERS An Empirical Model Our motivation model (next slide) was developed
25) Click Save Changes
When you signed up for Twitter, you entered some basic information and started following some other users. Before you go live with your profile and advertising your name, it is time to fine-tune your profile
Facebook WHAT ALREADY EXISTS? SETUP. 4 Activity. Video Training:
WHAT ALREADY EXISTS? A common trap to avoid is having multiple profiles set up by different people. These might have been done by employees, customers or complete strangers but can be really hard to manage
Chitika Insights The Value of Google Result Positioning
Chitika Insights The Value of Google Result Positioning June 7, 2013 A publication of 1 Introduction Being the top Google result for a key word or phrase is often seen as a tremendous achievement for a
THE PERFECT CUSTOMER
THE PERFECT CUSTOMER The Perfect Customer A perfect customer, or avatar, is a made up character that represents your ideal customer. This might be an exercise you re going to want to ignore, avoid or skip.
Provided by the author(s) and NUI Galway in accordance with publisher policies. Please cite the published version when available. Title Using social media for online television adaptation services at RTÉ
Social Media Calendar and Cheat Sheet
Social Media Calendar and Cheat Sheet August 2012 Sun Mon Tue Wed Thu Fri Sat 1 Maa Video Contest Starts 3 4 5 6 7 8 9 10 11 12 13 14 CSI 2.0 15 16 17 18 Boulder Darshan 19 20 21 22 23 24 25 26 27 28 29
2007 Census of Agriculture Non-Response Methodology
2007 Census of Agriculture Non-Response Methodology Will Cecere National Agricultural Statistics Service Research and Development Division, U.S. Department of Agriculture, 3251 Old Lee Highway, Fairfax,
Facebook Fan Page Secrets... 3 Section 1 Social Media Optimization... 4 Set Up Your Facebook Page... 4 Section 2 Fan Page Customization...
Facebook Fan Page Secrets... 3 Section 1 Social Media Optimization... 4 Set Up Your Facebook Page... 4 Section 2 Fan Page Customization... 6 Legitimize Your URL... 6 Customize the Look of Your Page...
Emotion analysis using text mining on social networks
Emotion analysis using text mining on social networks Rashmi Kumari 1, Mayura Sasane 2 1 Student,M.E-CSE, Parul Institute of Technology, Limda, Vadodara, India 2 Assistance Professor, M.E-CSE, Parul Institute
Some Challenging Problems in Mining Social Media
Some Challenging Problems in Mining Social Media Huan Liu Joint work with Shamanth Kumar Ali Abbasi Reza Zafarani Fred Morstatter Jiliang Tang Data Mining and Machine Learning Lab May 17, 2014 AI Forum
Start A Resident Referral Program
Finding potential renters is the most important marketing step to filling vacancies and keeping your property management business not just afloat but charging full speed ahead. But how do you reach those
Who Will Buy Your Book? HOW TO FIGURE OUT AND FIND YOUR TARGET AUDIENCE
Who Will Buy Your Book? HOW TO FIGURE OUT AND FIND YOUR TARGET AUDIENCE What you ll learn Why you must know who will buy (and love!) your book How to figure out who that is your target audience Where to
A FACEBOOK GUIDE FOR SALONS
A FACEBOOK GUIDE FOR SALONS A DIGITAL MARKETING GUIDE FOR SALONS Set Up Time 3/5 Fairly straightforward to set up, but you shouldn t rush through the process. Leave yourself plenty of time to get it right.
An Introduction to. By Paul J. Hurtado
An Introduction to By Paul J. Hurtado Where Birding Meets Science Talk Overview What is ebird? Navigating the ebird website: Exploring the data Contributing and organizing your own observations Additional
Verus. Khalid Alqinyah, Muhsin Gurel, Michael Mullen, Richard Tran, Phil Weber
Verus Khalid Alqinyah, Muhsin Gurel, Michael Mullen, Richard Tran, Phil Weber Schizophrenia A life long mental disorder involving a breakdown in relation between thought and emotion that leads to a faulty
Recommender Systems TIETS43 Collaborative Filtering
+ Recommender Systems TIETS43 Collaborative Filtering Fall 2017 Kostas Stefanidis kostas.stefanidis@uta.fi https://coursepages.uta.fi/tiets43/ selection Amazon generates 35% of their sales through recommendations
Issue: November 2012
Ad s s r e h s i l b Q Pu T OR P E R Ad Q S t u d i e s A Division of HARVEY RESEARCH, Inc. 6800 Pittsford-Palmyra Rd. Cedarwood Office Park, Suite 400 Fairport, New York 14450-3514 www.harveyresearch.com
Questionnaire Design with an HCI focus
Questionnaire Design with an HCI focus from A. Ant Ozok Chapter 58 Georgia Gwinnett College School of Science and Technology Dr. Jim Rowan Surveys! economical way to collect large amounts of data for comparison
Historic Landscape Project
Social Media Introduction Handout Why use social media? Infinite readership massive marketing potential (Even if your own Facebook page only has 40 Likes (followers), you can post something on it and ask
Secrets of Social Media on Purpose
The Ultimate Social Media Playbook for Soulful Storytellers How to supercharge your social media efforts and skyrocket your blog traffic for increased impact and income. Secrets of Social Media on Purpose
INTRODUCTION. Why 365 Days?
INTRODUCTION Why 365 Days? Many of you are probably asking yourself, Why do I need to have a 365 day plan? The answer is very simple. Other plans that offer a one, two or even three-month approach will
IT S COMPLICATED: SOCIAL MEDIA LEGAL CONCERNS. ISACA, North Texas Chapter April 10, 2014 Dallas, Texas
IT S COMPLICATED: SOCIAL MEDIA LEGAL CONCERNS ISACA, North Texas Chapter April 10, 2014 Dallas, Texas John Browning John Browning is a Partner at Lewis Brisbois Bisgaard & Smith in Dallas, Texas and a
LinkedIn Social Selling Linkedin Session 2 -Managing Your Settings Tagging And Groups
TRANSCRIPT Linkedin -Managing Your Settings Tagging And Groups Instructor: Brynne Tillman Copyright 2016 Social Media Marketing University MANAGING YOUR SETTINGS, TAGGING & GROUPS Welcome back to LinkedIn
7social media tips Who We Are Barry Hill James Trent
NEXT LEVEL Who We Are Barry Hill is the President and Founder of Bright Salt Media Labs. For more than a decade Barry has been partnering with churches, ministries and organizations to build digital communication
THE CHALLENGES OF SENTIMENT ANALYSIS ON SOCIAL WEB COMMUNITIES
THE CHALLENGES OF SENTIMENT ANALYSIS ON SOCIAL WEB COMMUNITIES Osamah A.M Ghaleb 1,Anna Saro Vijendran 2 1 Ph.D Research Scholar, Department of Computer Science, Sri Ramakrishna College of Arts and Science,(India)
Getting Started In Social Media For Animal Shelters and Rescue Groups. Christie Keith
Getting Started In Social Media For Animal Shelters and Rescue Groups Christie Keith Is this presentation for you? Yes, if you are not already using Facebook and Twitter at all, or only use it rarely and
2014 ResearchHack RECAP FOR PAPOR MINI-CONFERENCE. AAPOR s first EVER!!! Jennie Lai, Chuck Shuttles, Anna Wiencrot & Jordon Peugh June 13th, 2014
2014 ResearchHack RECAP FOR PAPOR MINI-CONFERENCE Jennie Lai, Chuck Shuttles, Anna Wiencrot & Jordon Peugh June 13th, 2014 AAPOR s first EVER!!! BACKGROUND: WHY A HACKATHON? It started with a conversation
Techniques for Sentiment Analysis survey
I J C T A, 9(41), 2016, pp. 355-360 International Science Press ISSN: 0974-5572 Techniques for Sentiment Analysis survey Anu Sharma* and Savleen Kaur** ABSTRACT A Sentiment analysis is a technique to analyze
WORKBOOK. 1 Page Marketing Plan
WORKBOOK 1 Page Marketing Plan We re so fortunate to be entrepreneurs today, with access to so many cheep, and sometimes free, ways to get the word out about what we do, and who we help. There is a social
Deepening Our Understanding of Social Media via Data Mining
Deepening Our Understanding of Social Media via Data Mining Huan Liu with DMML Members Data Mining and Machine Learning Lab October 6, 2014 LinkedIn 1 Social Media Mining by Cambridge University Press
If you have decided to start your own online business and are ready to go, first, there is one thing that you have to decide on.
Introduction If you have decided to start your own online business and are ready to go, first, there is one thing that you have to decide on. You have to pick a niche. Many people skip this step, or take
3. Data and sampling. Plan for today
3. Data and sampling Business Statistics Plan for today Reminders and introduction Data: qualitative and quantitative Quantitative data: discrete and continuous Qualitative data discussion Samples and
SOCIAL MEDIA UTILIZATION FOR ISLAMIC DA WAH
SOCIAL MEDIA UTILIZATION FOR ISLAMIC DA WAH Nur Hanis Jaafar and Siti Nur Syafiqah Umor Faculty of Information Management, Universiti Teknologi MARA (UiTM) Puncak Perdana Campus, UiTM Selangor, Malaysia
Day TWO: Find & Follow Building Your Community
Day TWO: Find & Follow Building Your Community The small print: All this material, whilst given freely for this course, is for your use only, and not for dissemination to anyone else, even inside your
PEAK GAMES IMPLEMENTS VOLTDB FOR REAL-TIME SEGMENTATION & PERSONALIZATION
PEAK GAMES IMPLEMENTS VOLTDB FOR REAL-TIME SEGMENTATION & PERSONALIZATION CASE STUDY TAKING ACTION BASED ON REAL-TIME PLAYER BEHAVIORS Peak Games is already a household name in the mobile gaming industry.
Inferring Social Media Users Demographics from Profile Pictures: A Face++ Analysis on Twitter Users
Jung, S.G., An, J., Kwak, H., Salminen, J. & Jansen, B.J. (2017). Inferring social media users demographics from profile pictures: A Face++ analysis on Twitter users. In Proceedings of The 17th International
On the Diversity of the Accountability Problem
On the Diversity of the Accountability Problem Machine Learning and Knowing Capitalism Bernhard Rieder Universiteit van Amsterdam Mediastudies Department Two types of algorithms Algorithms that make important
REPORT ON THE EUROSTAT 2017 USER SATISFACTION SURVEY
EUROPEAN COMMISSION EUROSTAT Directorate A: Cooperation in the European Statistical System; international cooperation; resources Unit A2: Strategy and Planning REPORT ON THE EUROSTAT 2017 USER SATISFACTION
Social Media and Networking
Social Media and Networking Student Workbook Student Workbook Page 1 Introduction: This workbook is a companion guide to be used with the online Social Media and Networking course. To develop expertise
A Guide to Sampling for Community Health Assessments and Other Projects
A Guide to Sampling for Community Health Assessments and Other Projects Introduction Healthy Carolinians defines a community health assessment as a process by which community members gain an understanding
MY SOCIAL MEDIA SEO SUCKS!
MY SOCIAL MEDIA SEO SUCKS! How to Use Social Media to Build Free Backlinks & Rank Your Website Faster Than Your Competition Ryan Radnor Table of Contents Introduction Why Social Media links The STEP BY
RED HAT TECH EXCHANGE HOUSE RULES
RED HAT TECH EXCHANGE HOUSE RULES 100% ATTENTION TAKE NOTES, NOT CALLS RECEIVE KNOWLEDGE, NOT MESSAGES MUTE NOTIFICATIONS FOR SLACK QQ WHATSAPP IMESSAGE EMAIL TELEGRAM SNAPCHAT FACEBOOK WEIBO HANGOUTS
Recommendation Systems UE 141 Spring 2013
Recommendation Systems UE 141 Spring 2013 Jing Gao SUNY Buffalo 1 Data Recommendation Systems users 1 3 4 3 5 5 4 5 5 3 3 2 2 2 1 items Goal Learn what a user might be interested in and recommend other
An IoT Based Real-Time Environmental Monitoring System Using Arduino and Cloud Service
Engineering, Technology & Applied Science Research Vol. 8, No. 4, 2018, 3238-3242 3238 An IoT Based Real-Time Environmental Monitoring System Using Arduino and Cloud Service Saima Zafar Emerging Sciences,
May 10, 2016, NSF-Census Research Network, Census Bureau. Research supported by NSF grant SES
A 2016 View of 2020 Census Quality, Costs, Benefits Bruce D. Spencer Department of Statistics and Institute for Policy Research Northwestern University May 10, 2016, NSF-Census Research Network, Census
Public Radio Navigates the Digital Revolution. Jacobs Media #PRTS2018
Public Radio Navigates the Digital Revolution Jacobs Media 2018 @fnjacobs #PRTS2018 Methodology 53 U.S. public radio stations N = 22,552 Interview dates: May 7 May 29, 2018 Most respondents are members
Certified Anesthesiologist Assistants Week 2018 Toolkit
Certified Anesthesiologist Assistants Week 2018 Toolkit Recognizing the contributions of Certified Anesthesiologist Assistants to perioperative patient care. National Certified Anesthesiologist Assistants
The Uses of Big Data in Social Research. Ralph Schroeder, Professor & MSc Programme Director
The Uses of Big Data in Social Research Ralph Schroeder, Professor & MSc Programme Director Hong Kong University of Science and Technology, March 6, 2013 Source: Leonard John Matthews, CC-BY-SA (http://www.flickr.com/photos/mythoto/3033590171)