This document discusses computational methods for intelligent matchmaking using social media data. It summarizes a case study called CMAD that aimed to identify weak ties from publicly available social media data related to a conference event. The study found it was possible to identify potential weak ties using Twitter data but Facebook data was not useful. Limitations included a small number of survey respondents. Future research opportunities include combining additional data sources and analytics methods like social network analysis to better identify tie strengths and validate results.
This is an academic poster that I will be presenting at the Information Seeking in Context conference 2016, hosted by the University of Zadar, Croatia.
It is called "The role of networking and social media during job search: an information behaviour perspective", and is based upon my PhD work to date. I am now entering my final year.
Slides to accompany Dr Louise Cooke's workshop session "An introduction to social network analysis" presented at DREaM Event 2.
For more information about the event, please visit http://lisresearch.org/dream-project/dream-event-2-workshop-tuesday-25-october-2011/
Tfsc disc 2014 si proposal (30 june2014)Han Woo PARK
Technological Forecasting and Social Change Special Issue
http://www.journals.elsevier.com/technological-forecasting-and-social-change/
Special issue title
Open (Big) Data as Social Change: Triple Helix Innovation toward Government 3.0
Associated conference
The 2nd Annual Asian Hub Conference on Triple Helix and Network Sciences (DISC 2014) on Data as Social Culture: Networked Innovation and Government 3.0, to be held on December 11-13, 2014, in Daegu and Gyeongbuk (Gyeongju), Rep. of Korea.
Call for Papers: http://www.slideshare.net/hanpark/disc-2014-cfp-v3
The conference is organized by Asia Triple Helix Society (ATHS). Point of contact: Secretary to Prof. Dr. Han Woo Park (info.disc2014@gmail.com), Department of Media & Communication, YeungNam University, 214-1, Dae-dong, Gyeongsan-si, Gyeongsangbuk-do, South Korea, Zip Code 712-749.
Associate Editors: Managing Guest Editors (MGE)
Wayne Weiai Xu, Doctoral Candidate, SUNY-Buffalo, USA, weiaixu@buffalo.edu
Dr. In Ho Cho, YeungNam University, Rep. of Korea, haihabacho@gmail.com
Important Dates
DISC 2014: 11 to 13 December 2014
Full paper submission: 1 March 2015
Review & Revision period: 1 September 2015
Online Publication: 1 December 2015
* We are also open to non-conference submissions to the special issue. However, the priority will be given to papers presented at the DISC 2014 and its associated seminars.
Who’s in the Gang? Revealing Coordinating Communities in Social MediaDerek Weber
Political astroturfing and organised trolling are online malicious behaviours with significant real-world effects. Common approaches examining these phenomena focus on broad campaigns rather than the small groups responsible. To reveal networks of cooperating accounts, we propose a novel temporal window approach that relies on account interactions and metadata alone. It detects groups of accounts engaging in behaviours that, in concert, execute different goal-based strategies, which we describe. Our approach is validated against two relevant datasets with ground truth data. See https://github.com/weberdc/find_hccs for code and data.
Presented at ASONAM'20 (2020 IEEE/ACM International Conference on Advances in Social Network Analysis and Mining).
Co-authored with Frank Neumann (University of Adelaide)
This is an academic poster that I will be presenting at the Information Seeking in Context conference 2016, hosted by the University of Zadar, Croatia.
It is called "The role of networking and social media during job search: an information behaviour perspective", and is based upon my PhD work to date. I am now entering my final year.
Slides to accompany Dr Louise Cooke's workshop session "An introduction to social network analysis" presented at DREaM Event 2.
For more information about the event, please visit http://lisresearch.org/dream-project/dream-event-2-workshop-tuesday-25-october-2011/
Tfsc disc 2014 si proposal (30 june2014)Han Woo PARK
Technological Forecasting and Social Change Special Issue
http://www.journals.elsevier.com/technological-forecasting-and-social-change/
Special issue title
Open (Big) Data as Social Change: Triple Helix Innovation toward Government 3.0
Associated conference
The 2nd Annual Asian Hub Conference on Triple Helix and Network Sciences (DISC 2014) on Data as Social Culture: Networked Innovation and Government 3.0, to be held on December 11-13, 2014, in Daegu and Gyeongbuk (Gyeongju), Rep. of Korea.
Call for Papers: http://www.slideshare.net/hanpark/disc-2014-cfp-v3
The conference is organized by Asia Triple Helix Society (ATHS). Point of contact: Secretary to Prof. Dr. Han Woo Park (info.disc2014@gmail.com), Department of Media & Communication, YeungNam University, 214-1, Dae-dong, Gyeongsan-si, Gyeongsangbuk-do, South Korea, Zip Code 712-749.
Associate Editors: Managing Guest Editors (MGE)
Wayne Weiai Xu, Doctoral Candidate, SUNY-Buffalo, USA, weiaixu@buffalo.edu
Dr. In Ho Cho, YeungNam University, Rep. of Korea, haihabacho@gmail.com
Important Dates
DISC 2014: 11 to 13 December 2014
Full paper submission: 1 March 2015
Review & Revision period: 1 September 2015
Online Publication: 1 December 2015
* We are also open to non-conference submissions to the special issue. However, the priority will be given to papers presented at the DISC 2014 and its associated seminars.
Who’s in the Gang? Revealing Coordinating Communities in Social MediaDerek Weber
Political astroturfing and organised trolling are online malicious behaviours with significant real-world effects. Common approaches examining these phenomena focus on broad campaigns rather than the small groups responsible. To reveal networks of cooperating accounts, we propose a novel temporal window approach that relies on account interactions and metadata alone. It detects groups of accounts engaging in behaviours that, in concert, execute different goal-based strategies, which we describe. Our approach is validated against two relevant datasets with ground truth data. See https://github.com/weberdc/find_hccs for code and data.
Presented at ASONAM'20 (2020 IEEE/ACM International Conference on Advances in Social Network Analysis and Mining).
Co-authored with Frank Neumann (University of Adelaide)
Med2028 m media research methods & proposal designMeezan Bank
I have complete copy of this assignment. If you want to have complete draft of this work with no plagiarism, then contact me at my email id: projectwork185@gmail.com
Information Contagion through Social Media: Towards a Realistic Model of the ...Axel Bruns
Paper by Axel Bruns, Patrik Wikström, Peta Mitchell, Brenda Moon, Felix Münch, Lucia Falzon, and Lucy Resnyansky presented at the ACSPRI 2016 conference, Sydney, 19-22 July 2016/
Fuzzy AndANN Based Mining Approach Testing For Social Network AnalysisIJERA Editor
Fast and Appropriate Social Network Analysis (SNA) tools ,techniques, are required to collect and classify
opinion scores on social networksites , as a grouping on wrong opinion may create problems for a society or
country . Social Network Analysis (SNA) is popular means for researcher as the number of users and groups
increasing day by day on that social sites , and a large group may influence other.In this paper, we
recommendhybrid model of opinion recommendation systems, for single user and for collective community
respectively, formed on social liking and influence network theory. By collecting thedata of user social networks
and preferenceslike, we designed aimproved hybrid prototype to imitate the social influence by like and sharing
the information among groups.The significance of this paper to analyze the suitability of ANN and Fuzzy sets
method in a hybrid manner for social web sites classifications, First, we intend to use Artificial Neural
Network(ANN)techniques in social media data classification by using some contemporary methods different
than the conventional methods of statistics and data analysis, in next we want to propagate the fuzzy approach
as a way to overcome the uncertainity that is always present in social media analysis . We give a brief overview
of the main ideas and recent results of social networks analysis , and we point to relationships between the two
social network analysis and classification approaches .This researchsuggests a hybrid classification model build
on fuzzy and artificial neural network (HFANN). Information Gain and three popular social sites are used to
collect information depicting features that are then used to train and test the proposed methods . This neoteric
approach combines the advantages of ANN and Fuzzy sets in classification accuracy with utilizing social data
and knowledge base available in the hate lexicons.
Part 1 of the "Making Sense of Twitter: Quantitative Analysis Using Twapperkeeper and Other Tools" workshop, presented at the Communities & Technologies 2011 conference, Brisbane, 29 June 2011.
Social Media in Australia: A ‘Big Data’ Perspective on TwitterAxel Bruns
Invited presentation at the University of Melbourne, 4 April 2017.
Twitter research to date has focussed mainly on the study of isolated events, as described for example by specific hashtags or keywords relating to elections, natural disasters, public events, and other moments of heightened activity in the network. This limited focus is determined in part by the limitations placed on large-scale access to Twitter data by Twitter, Inc. itself. This research presents the first ever comprehensive study of a national Twittersphere as an entity in its own right. It examines the structure of the follower network amongst some 4 million Australian Twitter accounts and the dynamics of their day-to-day activities, and explores the Australian Twittersphere’s engagement with specific recent events.
How data fellows open doors to data careers:
This talk will draw on findings from a Data Fellowship programme that was established in 2013 through the University of Manchester’s Q-Step programme. The data fellows are drawn from social science undergraduate degrees and since starting with 19 in 2014 we have now placed 330 student into around 60 organisations to do data-led research projects. The results have been published in articles and a book and Jackie will provide insight into these placements and talk about how the programme is opening up opportunities for social science graduates to enter data and statistical careers. She has developed a ‘research and analytical skills’ and ‘professional skills’ framework based on British Academy and LinkedIn and McKinsey reports. She is currently talking to employers about their ‘data skills’ needs and she is hoping her current research will result in a data skills framework that is more inclusive and not focused predominantly on STEM subjects. Her aim is to contribute to creating a more diverse talent pipeline into data careers.
Med2028 m media research methods & proposal designMeezan Bank
I have complete copy of this assignment. If you want to have complete draft of this work with no plagiarism, then contact me at my email id: projectwork185@gmail.com
Information Contagion through Social Media: Towards a Realistic Model of the ...Axel Bruns
Paper by Axel Bruns, Patrik Wikström, Peta Mitchell, Brenda Moon, Felix Münch, Lucia Falzon, and Lucy Resnyansky presented at the ACSPRI 2016 conference, Sydney, 19-22 July 2016/
Fuzzy AndANN Based Mining Approach Testing For Social Network AnalysisIJERA Editor
Fast and Appropriate Social Network Analysis (SNA) tools ,techniques, are required to collect and classify
opinion scores on social networksites , as a grouping on wrong opinion may create problems for a society or
country . Social Network Analysis (SNA) is popular means for researcher as the number of users and groups
increasing day by day on that social sites , and a large group may influence other.In this paper, we
recommendhybrid model of opinion recommendation systems, for single user and for collective community
respectively, formed on social liking and influence network theory. By collecting thedata of user social networks
and preferenceslike, we designed aimproved hybrid prototype to imitate the social influence by like and sharing
the information among groups.The significance of this paper to analyze the suitability of ANN and Fuzzy sets
method in a hybrid manner for social web sites classifications, First, we intend to use Artificial Neural
Network(ANN)techniques in social media data classification by using some contemporary methods different
than the conventional methods of statistics and data analysis, in next we want to propagate the fuzzy approach
as a way to overcome the uncertainity that is always present in social media analysis . We give a brief overview
of the main ideas and recent results of social networks analysis , and we point to relationships between the two
social network analysis and classification approaches .This researchsuggests a hybrid classification model build
on fuzzy and artificial neural network (HFANN). Information Gain and three popular social sites are used to
collect information depicting features that are then used to train and test the proposed methods . This neoteric
approach combines the advantages of ANN and Fuzzy sets in classification accuracy with utilizing social data
and knowledge base available in the hate lexicons.
Part 1 of the "Making Sense of Twitter: Quantitative Analysis Using Twapperkeeper and Other Tools" workshop, presented at the Communities & Technologies 2011 conference, Brisbane, 29 June 2011.
Social Media in Australia: A ‘Big Data’ Perspective on TwitterAxel Bruns
Invited presentation at the University of Melbourne, 4 April 2017.
Twitter research to date has focussed mainly on the study of isolated events, as described for example by specific hashtags or keywords relating to elections, natural disasters, public events, and other moments of heightened activity in the network. This limited focus is determined in part by the limitations placed on large-scale access to Twitter data by Twitter, Inc. itself. This research presents the first ever comprehensive study of a national Twittersphere as an entity in its own right. It examines the structure of the follower network amongst some 4 million Australian Twitter accounts and the dynamics of their day-to-day activities, and explores the Australian Twittersphere’s engagement with specific recent events.
How data fellows open doors to data careers:
This talk will draw on findings from a Data Fellowship programme that was established in 2013 through the University of Manchester’s Q-Step programme. The data fellows are drawn from social science undergraduate degrees and since starting with 19 in 2014 we have now placed 330 student into around 60 organisations to do data-led research projects. The results have been published in articles and a book and Jackie will provide insight into these placements and talk about how the programme is opening up opportunities for social science graduates to enter data and statistical careers. She has developed a ‘research and analytical skills’ and ‘professional skills’ framework based on British Academy and LinkedIn and McKinsey reports. She is currently talking to employers about their ‘data skills’ needs and she is hoping her current research will result in a data skills framework that is more inclusive and not focused predominantly on STEM subjects. Her aim is to contribute to creating a more diverse talent pipeline into data careers.
Researching Social Media – Big Data and Social Media AnalysisFarida Vis
Researching Social Media – Big Data and Social Media Analysis, presentation for the Social Media for Researchers: A Sheffield Universities Social Media Symposium, 23 September 2014
Social networking sites are a significant source of information to know the behavior of users and to know
what is occupying society of all ages and accordingly helpful information can be provided to specialists
and decision-makers. According to official sources, 98.43% of Saudi youth use social networking sites. The
study and analysis of social media data are done to provide the necessary information to increase
investment opportunities within the Kingdom of Saudi Arabia, by studying and analyzing what people
occupy on the communication sites through their tweets about the labor market and investment. Given the
huge volume of data and also its randomness, a survey of the data will be done and collected from through
keywords, the priority of arranging the data, and recording it as (positive - negative - mixed). The study
analysis and conclusion will be based on data-mining and its techniques of analysis and deduction
.
INCREASING THE INVESTMENT’S OPPORTUNITIES IN KINGDOM OF SAUDI ARABIA BY STUDY...ijcsit
Social networking sites are a significant source of information to know the behavior of users and to know
what is occupying society of all ages and accordingly helpful information can be provided to specialists
and decision-makers. According to official sources, 98.43% of Saudi youth use social networking sites. The
study and analysis of social media data are done to provide the necessary information to increase
investment opportunities within the Kingdom of Saudi Arabia, by studying and analyzing what people
occupy on the communication sites through their tweets about the labor market and investment. Given the
huge volume of data and also its randomness, a survey of the data will be done and collected from through
keywords, the priority of arranging the data, and recording it as (positive - negative - mixed). The study
analysis and conclusion will be based on data-mining and its techniques of analysis and deduction.
Distributed Link Prediction in Large Scale Graphs using Apache SparkAnastasios Theodosiou
Online social networks (OSNs) such as Facebook, Instagram, Twitter, and LinkedIn are aware of high acceptance since they enable users to share digital content (links, pictures, videos), express or share opinions and expand their social circle, by making new friends. All these kinds of interactions that users participate in the lead to the evolution and expansion of social networks over time. OSNs support users, providing them with friend recommendations based on the existing explicit friendship network but also to their preferences resulting from their interaction with the net which they gradually build. Link prediction methods try to predict the likelihood of a future connection between two nodes in a given network. This can be used in biological networks to infer protein-protein interactions or to suggest possible friends to a user in an OSN. In e-commerce it can help to build recommendation systems such as the "people who bought this may also buy" feature, e.g., on Amazon and in the security domain link prediction can help to identify hidden groups of people who use potential violence and criminals. Due to the massive amounts of data that is collected today, the need for scalable approaches arises to this problem. The purpose of this diploma thesis is to experiment and use various techniques of machine learning, both supervised and unsupervised, to predict links to a network of academic papers using document similarity metrics based on the characteristics of the nodes but also other structural features, based on the network. Experimentation and implementation of the application took place using Apache Spark to manage the large data volume using the Scala programming language.
This is a presentation that describes at a high level some of the work we've been performing related to NodeXL and it's use to understand social media networks.
An overview of a social psychological approach to the design of social technologies, with design principles and a brief review of how I applied these principles to several R&D projects in the past few years.
This presentation was given to the Seattle chapter of IxDA in October 2009.
These slides were part of the kickoff for the Social Computing Collaborative group at the University of Minnesota - Jan. 2011. Each participant presented a single slide as part of their introduction of themselves and their social computing research interest areas.
Data Management and Broader Impacts: a holistic approachMegan O'Donnell
[please download to view at full resolution]
The National Science Foundation’s (NSF) Broader Impacts Criterion asks scientists to frame their research beyond “science for science’s sake.” Examining data and data management through a Broader Impacts lens highlights the benefits of good data management, data management plans (DMPs), and strengthens the argument for better Data Information Literacy (DIL) in the sciences.
Working with Social Media Data: Ethics & good practice around collecting, usi...Nicola Osborne
Slides from a workshop delivered for the University of Edinburgh Digital Scholarship programme, on 18th October 2017. For further information on the programme see: http://www.digital.cahss.ed.ac.uk/ or #DigScholEd. If you are interested in hosting a similar workshop, or adapting these slides please contact me: nicola.osborne@ed.ac.uk.
This presentation was provided by Daniella Lowenberg of the California Digital Library during the NISO Virtual Conference, Advancing Altmetrics, held on Wednesday, December 13, 2017.
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Computational methods for intelligent matchmaking for knowledge work
1. COMPUTATIONAL METHODS FOR INTELLIGENT MATCHMAKING FOR
KNOWLEDGE WORK – CASE CMAD
Jayesh Prakash Gupta, Tampere University of Technology, Finland
Jari Jussila, Tampere University of Technology, Finland
Ekaterina Olshannikova, Tampere University of Technology, Finland
Karan Menon, Tampere University of Technology, Finland
Jukka Huhtamäki, Tampere University of Technology, Finland
Thomas Olsson, Tampere University of Technology, Finland
Prof. Ravi Vatrapu, Copenhagen Business School, Denmark
Prof. Hannu Kärkkäinen, Tampere University of Technology, Finland
References
Granovetter, Mark S. "The strength of weak ties." American journal of sociology 78.6 (1973): 1360-1380.
Marsden, Peter V., and Karen E. Campbell. "Measuring tie strength." Soc. F. 63 (1984): 482.
Gilbert, Eric, and Karrie Karahalios. "Predicting tie strength with social media." Proceedings of the SIGCHI conference on human factors in computing systems. ACM, 2009.
Gupta, Jayesh Prakash, et al. "Identifying weak ties from publicly available social media data in an event." Proceedings of the 20th International Academic Mindtrek Conference. ACM, 2016.
Availability of Big Social DATA
Results
• It is possible to identify potential weak ties from using the publically available social
media data
• Facebook data was not useful in identifying weak ties
• Twitter was helpful in identifying the potential weak ties.
• Temporal pattern of different social media channels use was different for Twitter
and Facebook
Limitations:
• Insufficient number of survey respondents to draw any statistically significant result
• Only some of the possible methods for weak tie identification were used
Future Research
• Combined other kind of data sources and methods for weak tie identification
• Move towards utilizing Big Social Data
• Utilize new analytics methods like Social Set Analysis
• Validate the results using large number of survey respondents
• The next steps:
Please signup to be part of the future studies by providing your details on the
following link or by scanning the QR code
http://bit.ly/2jGOw64
Part of the COBWEB research project
Funded by Academy of Finland (http://www.aka.fi).
Presented Jan 23, 2017 at CMAD2017, University of Tampere, Tampere.
Match Making in Professional Life Some Current Match Making Apps
grip.events shapr.net
Case CMAD 2016 Results
Interaction networks
Twitter
Facebook
Possible clusters based on Twitter Data
Modularity class Cluster name
1 Personal branding
2 Employee advocacy
3 Drawings and infographics
5 **Broadly about cmadfi event
6 Community manager
7 Communications
10 *Reporting on CMADFI event
15 Customer service
16 Project
17 *Outsider greetings
18 Tekes
20 Knowledge management
21 Jyväskylän energia
Why is tie strength (weak and strong ties)
important? Why social media / BSD
analytics?
Examples of matchmaking- related needs (which are rarely made use of e.g.
in current matchmaking tools):
Efficient spreading of information/research results/etc. to as large group of
relevant people as possible (strong ties, authoritative/influencing persons)
Identify new viewpoints and knowledge to problems or for new innovations /
new interdisciplinary research (weak ties; both similar and new knowledge)
Find relevant persons in professional events and conferences (find persons
with novel / complementary expertise / persons with influence)
Find interesting collaborators to a research project
Different Kind of Ties
• Strong ties: Strong ties
are the ones that a
person really trust. For
eg. family
• Weak ties: Refers to
weak links which may be
known.Eg.
acquaintances, friend of
friend
• Weak ties are very
useful in spreading novel
information.
New Possibilities with Big Social DATASocial Media Data , BSD and SNA (Social
Network Analysis) in tie strength
evaluation and matchmaking?
Social Media data for example Facebook
Friends, Twitter Follower/ Followee, have
been used to identify the actual social
network and tie strength of a person.
Additionally, it is possible to use
conversation data on social media to
evaluate tie strength and help in
identifying different kind of ties. SNA is
one possible way to do this.
SNA enables in easier identification of
different influential people and the
potential sub- communities in a network.
BSD can e.g. enable using the historical
conversation / other social data about
conference/s to provide preliminary
deductions about participants tie strength
and the possible useful ties, their common
or complimentary interests. For example
co-occurrence of conference participants.