This document summarizes a presentation on the evolution of social media research. It discusses how early research focused on analyzing small datasets from platforms like Flickr and YouTube. Over time, as Twitter grew in popularity, researchers began analyzing larger Twitter datasets containing millions of tweets. However, these datasets still had limitations due to biases in the data available through APIs. The document also discusses critiques of "Big Data" approaches and the need for research to be question-driven rather than just analyzing available data. It emphasizes understanding the limitations of datasets and being transparent about how the data was collected and potential biases.
ESRC Research Methods Festival - From Flickr to Snapchat: The challenge of an...Farida Vis
From Flickr to Snapchat: The challenge of analysing images on social media. Presentation part of the 'Challenges/Opportunities of Using Social Media for Social Science Research' panel. 9th of July 2014
Picturing the Social: Talk for Transforming Digital Methods Winter SchoolFarida Vis
This talk highlights the work of the Visual Social Media Lab and the Picturing the Social project. It summarises the key research questions and aims of the project. It highlights the value of interdisciplinarity and working closely with industry in this area. It also focuses on the way in which me might study different types of structures involved in the circulation and the scopic regimes that make social media images more or less visible. It also tries to unpack how we can start to think about APIs as 'method' and looks at the different ways in which we can get access to different kinds of social media image data. Both through public ('free') APIs and ('pay for') firehose data.
Presentation for: Masterclass 19: Using social media in public engagement for the Public Engagement & Impact Team at The University of Sheffield, 26 November 2014.
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
Twitter analytics: some thoughts on sampling, tools, data, ethics and user re...Farida Vis
Keynote delivered at the SRA Social Media in Social Research conference, London, 24 June, 2013. The presentation highlights some thoughts on sampling, tools, data, ethics and user requirements for Twitter analytics, including an overview of a series of recent tools.
ESRC Research Methods Festival - From Flickr to Snapchat: The challenge of an...Farida Vis
From Flickr to Snapchat: The challenge of analysing images on social media. Presentation part of the 'Challenges/Opportunities of Using Social Media for Social Science Research' panel. 9th of July 2014
Picturing the Social: Talk for Transforming Digital Methods Winter SchoolFarida Vis
This talk highlights the work of the Visual Social Media Lab and the Picturing the Social project. It summarises the key research questions and aims of the project. It highlights the value of interdisciplinarity and working closely with industry in this area. It also focuses on the way in which me might study different types of structures involved in the circulation and the scopic regimes that make social media images more or less visible. It also tries to unpack how we can start to think about APIs as 'method' and looks at the different ways in which we can get access to different kinds of social media image data. Both through public ('free') APIs and ('pay for') firehose data.
Presentation for: Masterclass 19: Using social media in public engagement for the Public Engagement & Impact Team at The University of Sheffield, 26 November 2014.
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
Twitter analytics: some thoughts on sampling, tools, data, ethics and user re...Farida Vis
Keynote delivered at the SRA Social Media in Social Research conference, London, 24 June, 2013. The presentation highlights some thoughts on sampling, tools, data, ethics and user requirements for Twitter analytics, including an overview of a series of recent tools.
What Actor-Network Theory (ANT) and digital methods can do for data journalis...Liliana Bounegru
Slides from a talk I gave at the University of Ghent on 21 October 2014 about how Actor-Network Theory (ANT) and digital methods can be used to study and inform data journalism.
GitHub as Transparency Device in Data Journalism, Open Data and Data ActivismLiliana Bounegru
Slides from presentation of research agenda around uses of GitHub in journalism at the Digital Methods Summer School 2015. More details here: http://lilianabounegru.org/2015/07/08/github-as-transparency-device-in-data-journalism-open-data-and-data-activism/
Doing Social and Political Research in a Digital Age: An Introduction to Digi...Liliana Bounegru
Lecture given at the National Center of Competence in Research: Challenges to Democracy in the 21st Century, 5 November 2015, Zürich University, Zürich, Switzerland
Doing Digital Methods: Some Recent Highlights from Winter and Summer SchoolsLiliana Bounegru
Talk given at the Digital Methods Winter School 2017 at the University of Amsterdam. It presents a selection of projects developed at the 2016 Digital Methods Winter and Summer Schools (www.digitalmethods.net).
Journalists today are faced with an overwhelming abundance of data – from large collections of leaked documents, to public databases about lobbying or government spending, to ‘big data’ from social networks such as Twitter and Facebook. To stay relevant to society journalists are learning to process this data and separate signal from noise in order to provide valuable insights to their readers. This talk will address questions like: What is the potential of data journalism? Why is it relevant to society? And how can you get started?
For my final year project I used data analysis techniques to investigate user behavior pattern recognition in respect of similar interests and culture versus offline geographical location. This was an out-of-the-box topic, which I selected due to my love on Data Analysis, in respect of the Social Network Analysis in the Internet era.
Mapping Issues with the Web: An Introduction to Digital MethodsJonathan Gray
Slides from talk on "Mapping Issues with the Web: An Introduction to Digital Methods" at Tow Center for Digital Journalism, Columbia University, 23rd September 2014. Further details at: http://jonathangray.org/2014/09/10/mapping-issues-with-web-columbia/
Data Journalism and the Remaking of Data InfrastructuresLiliana Bounegru
Talk given at the “Evidence and the Politics of Policymaking” Conference, University of Bath, 14th September 2016, on the basis of my PhD research at the University of Groningen and University of Ghent.
http://www.bath.ac.uk/ipr/events/news-0230.html.
Coding Social Imagery: Learning from a #selfie #humor Image Set from InstagramShalin Hai-Jew
Social media messaging has long been harnessed to inform faculty about their respective learners. The textual channel is often used because of the ease of interpretation and analysis. Social imagery—tagged images, #selfies, grouped imagery, and others—has been less used, in part because images are more complex and multi-meaninged to analyze. Also, there are not many generalist models that inform how to code or even understand social imagery in an emergent way. (There are large-scale computational means to interpret online images, such as the AlchemyAPI of IBM Watson, for various types of feature extractions. There are ways to code imagery based on specific research questions in particular fields-of-practice.)
The presenter recently analyzed a 941-image #selfie + #humor image set from Instagram, with three main research questions:
What does identity-based humor look like in terms of a #selfie #humor- tagged image set from the Instagram photo-sharing mobile app?
Do more modern forms of mediated social humor link to more traditional forms theoretically? Is it possible to apply the Humor Styles Model to the images from the #selfie #humor Instagram image set to better understand #selfie #humor?
What are some constructive and systematized ways to analyze social image sets manually (with some computational support)?
This digital poster session will highlight some of the initial research findings (forthcoming in a near-future publication) and share insights about effectively coding social imagery in a bottom-up and emergent way.
Web 2.0 Collective Intelligence - How to use collective intelligence techniqu...Paul Gilbreath
Source: http://www.helioteixeira.org/ How to use Collective Intelligence techniques to ensure that your web application can extract valuable data from its usage and deliver that value right back to the users. (MODULE 1)
Eavesdropping on the Twitter Microblogging SiteShalin Hai-Jew
Research analysts go to Twitter to capture the general trends of public conversations, identify and profile influential accounts, and extract subgroups within larger collectives and larger discourses; they also go to eavesdrop on individual self-talk and individual-to-individual conversations. So what is technically in your tweets, asked Dave Rosenberg famously in a CNET article (2010). The answer: a whole lot more than 140 characters. How are the most influential social media accounts identified through #hashtag graphs? How are themes extracted? How are sentiments understood? How can users be profiled through their Tweetstreams? How can locations be mapped in terms of the Twitter conversations occurring in particular physical areas? How can live and trending issues be identified and categorized in terms of sentiment (positive, negative, and neutral)? This presentation will summarize some of the free and open-source tools as well as commercial and proprietary ones that enable increased knowability.
RUNNING HEAD: BIG DATA IN SOCIAL MEDIA 1
BIG DATA IN SOCIAL MEDIA 3
Big Data in Social Media
By definition, Big Data can simply be termed as voluminous data. In more specific definitions, it can be termed as that which is large, complex and fast and a s a result, is not in a position to be processed using the typical traditional methods of data processing. The volume, variety, velocity, variability and veracity are used in the categorizing of data as big data. With the development in technology, and the continued incorporation of these technological sources into our day to day lives, the collected data through the Internet of Things among other information systems has resulted in big data (Ivanov, 2018). One such areas where Big data is found is in the social media platforms. As opposed to the olden days, currently, more and more people and companies are using social media daily to achieve their specific objectives and goals, it is estimated that social media platforms like Facebook produce data as big as 500+ terabytes in a s ingle day!
Most of these data in the social media are as a result of the videos, photos, messages and comments being shared across the media platforms. Not only do individuals use social media to keep in touch, but companies also use it in a concept called social media marketing. Through the media, and using big data analytics, companies are able to map out consumer behavior through what they like and what they share (Nicora, 2019). They use these platforms to reach their target audiences and at the same time use them to get feedback from their clients. As a result, the amount of data from social media platforms is not only voluminous, it is also heterogenous in the sense that it contains both nominal and numeral values from different places, it is variable in that it has unpredictable flow, it is fast because it is collected in real time. This qualifies the data to be Big Data and requires big data analytics to process.
References
Ivanov I. (2018) What is Big Data Analytics on Social Media? Iocowise. Retrieved from https://locowise.com/blog/what-is-big-data-analytics-on-social-media
Nicora R. (2019) How is big data impacting social media? Medium. Retrieved from https://medium.com/dative-io/how-is-big-data-impacting-social-media-df31aa3f66f6
1
4
Title
Student Name
Ashford University
GEN103: Information Literacy
Instructor Name
Month Day, Year
Title
Research Question:
Replace these instructions with your research question. Incorporate any feedback your instructor provided on your week 1 assignment. To learn how to view the comments on your papers watch the Waypoint: Accessing Assignment Feedback video.
Thesis Statement:
Replace these instructions with your thesis statement. Refer to the Writing Center’.
Big Data for International DevelopmentAlex Rascanu
Alex Rascanu delivered the "Big Data for International Development" presentation at the International Development Conference that took place on February 7, 2015 at University of Toronto Scarborough.
What Actor-Network Theory (ANT) and digital methods can do for data journalis...Liliana Bounegru
Slides from a talk I gave at the University of Ghent on 21 October 2014 about how Actor-Network Theory (ANT) and digital methods can be used to study and inform data journalism.
GitHub as Transparency Device in Data Journalism, Open Data and Data ActivismLiliana Bounegru
Slides from presentation of research agenda around uses of GitHub in journalism at the Digital Methods Summer School 2015. More details here: http://lilianabounegru.org/2015/07/08/github-as-transparency-device-in-data-journalism-open-data-and-data-activism/
Doing Social and Political Research in a Digital Age: An Introduction to Digi...Liliana Bounegru
Lecture given at the National Center of Competence in Research: Challenges to Democracy in the 21st Century, 5 November 2015, Zürich University, Zürich, Switzerland
Doing Digital Methods: Some Recent Highlights from Winter and Summer SchoolsLiliana Bounegru
Talk given at the Digital Methods Winter School 2017 at the University of Amsterdam. It presents a selection of projects developed at the 2016 Digital Methods Winter and Summer Schools (www.digitalmethods.net).
Journalists today are faced with an overwhelming abundance of data – from large collections of leaked documents, to public databases about lobbying or government spending, to ‘big data’ from social networks such as Twitter and Facebook. To stay relevant to society journalists are learning to process this data and separate signal from noise in order to provide valuable insights to their readers. This talk will address questions like: What is the potential of data journalism? Why is it relevant to society? And how can you get started?
For my final year project I used data analysis techniques to investigate user behavior pattern recognition in respect of similar interests and culture versus offline geographical location. This was an out-of-the-box topic, which I selected due to my love on Data Analysis, in respect of the Social Network Analysis in the Internet era.
Mapping Issues with the Web: An Introduction to Digital MethodsJonathan Gray
Slides from talk on "Mapping Issues with the Web: An Introduction to Digital Methods" at Tow Center for Digital Journalism, Columbia University, 23rd September 2014. Further details at: http://jonathangray.org/2014/09/10/mapping-issues-with-web-columbia/
Data Journalism and the Remaking of Data InfrastructuresLiliana Bounegru
Talk given at the “Evidence and the Politics of Policymaking” Conference, University of Bath, 14th September 2016, on the basis of my PhD research at the University of Groningen and University of Ghent.
http://www.bath.ac.uk/ipr/events/news-0230.html.
Coding Social Imagery: Learning from a #selfie #humor Image Set from InstagramShalin Hai-Jew
Social media messaging has long been harnessed to inform faculty about their respective learners. The textual channel is often used because of the ease of interpretation and analysis. Social imagery—tagged images, #selfies, grouped imagery, and others—has been less used, in part because images are more complex and multi-meaninged to analyze. Also, there are not many generalist models that inform how to code or even understand social imagery in an emergent way. (There are large-scale computational means to interpret online images, such as the AlchemyAPI of IBM Watson, for various types of feature extractions. There are ways to code imagery based on specific research questions in particular fields-of-practice.)
The presenter recently analyzed a 941-image #selfie + #humor image set from Instagram, with three main research questions:
What does identity-based humor look like in terms of a #selfie #humor- tagged image set from the Instagram photo-sharing mobile app?
Do more modern forms of mediated social humor link to more traditional forms theoretically? Is it possible to apply the Humor Styles Model to the images from the #selfie #humor Instagram image set to better understand #selfie #humor?
What are some constructive and systematized ways to analyze social image sets manually (with some computational support)?
This digital poster session will highlight some of the initial research findings (forthcoming in a near-future publication) and share insights about effectively coding social imagery in a bottom-up and emergent way.
Web 2.0 Collective Intelligence - How to use collective intelligence techniqu...Paul Gilbreath
Source: http://www.helioteixeira.org/ How to use Collective Intelligence techniques to ensure that your web application can extract valuable data from its usage and deliver that value right back to the users. (MODULE 1)
Eavesdropping on the Twitter Microblogging SiteShalin Hai-Jew
Research analysts go to Twitter to capture the general trends of public conversations, identify and profile influential accounts, and extract subgroups within larger collectives and larger discourses; they also go to eavesdrop on individual self-talk and individual-to-individual conversations. So what is technically in your tweets, asked Dave Rosenberg famously in a CNET article (2010). The answer: a whole lot more than 140 characters. How are the most influential social media accounts identified through #hashtag graphs? How are themes extracted? How are sentiments understood? How can users be profiled through their Tweetstreams? How can locations be mapped in terms of the Twitter conversations occurring in particular physical areas? How can live and trending issues be identified and categorized in terms of sentiment (positive, negative, and neutral)? This presentation will summarize some of the free and open-source tools as well as commercial and proprietary ones that enable increased knowability.
RUNNING HEAD: BIG DATA IN SOCIAL MEDIA 1
BIG DATA IN SOCIAL MEDIA 3
Big Data in Social Media
By definition, Big Data can simply be termed as voluminous data. In more specific definitions, it can be termed as that which is large, complex and fast and a s a result, is not in a position to be processed using the typical traditional methods of data processing. The volume, variety, velocity, variability and veracity are used in the categorizing of data as big data. With the development in technology, and the continued incorporation of these technological sources into our day to day lives, the collected data through the Internet of Things among other information systems has resulted in big data (Ivanov, 2018). One such areas where Big data is found is in the social media platforms. As opposed to the olden days, currently, more and more people and companies are using social media daily to achieve their specific objectives and goals, it is estimated that social media platforms like Facebook produce data as big as 500+ terabytes in a s ingle day!
Most of these data in the social media are as a result of the videos, photos, messages and comments being shared across the media platforms. Not only do individuals use social media to keep in touch, but companies also use it in a concept called social media marketing. Through the media, and using big data analytics, companies are able to map out consumer behavior through what they like and what they share (Nicora, 2019). They use these platforms to reach their target audiences and at the same time use them to get feedback from their clients. As a result, the amount of data from social media platforms is not only voluminous, it is also heterogenous in the sense that it contains both nominal and numeral values from different places, it is variable in that it has unpredictable flow, it is fast because it is collected in real time. This qualifies the data to be Big Data and requires big data analytics to process.
References
Ivanov I. (2018) What is Big Data Analytics on Social Media? Iocowise. Retrieved from https://locowise.com/blog/what-is-big-data-analytics-on-social-media
Nicora R. (2019) How is big data impacting social media? Medium. Retrieved from https://medium.com/dative-io/how-is-big-data-impacting-social-media-df31aa3f66f6
1
4
Title
Student Name
Ashford University
GEN103: Information Literacy
Instructor Name
Month Day, Year
Title
Research Question:
Replace these instructions with your research question. Incorporate any feedback your instructor provided on your week 1 assignment. To learn how to view the comments on your papers watch the Waypoint: Accessing Assignment Feedback video.
Thesis Statement:
Replace these instructions with your thesis statement. Refer to the Writing Center’.
Big Data for International DevelopmentAlex Rascanu
Alex Rascanu delivered the "Big Data for International Development" presentation at the International Development Conference that took place on February 7, 2015 at University of Toronto Scarborough.
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.
Data Science For Social Good: Tackling the Challenge of HomelessnessAnita Luthra
A talk presented at the Champions Leadership Conference Series - leveraging data provided by New York City’s Department of Homeless Services, software vendor Tibco partnered with SumAll.Org to help tackle the societal challenge of homelessness in New York City.
2010 Catalyst Conference - Trends in Social Network AnalysisMarc Smith
Review of trends related to social network analysis in the enterprise. Presented at the 2010 Catalyst Conference in San Diego, CA july 29, 2010. Presented with Mike Gotta, Gartner Group.
Lecture series: Using trace data or subjective data, that is the question dur...Bart Rienties
In this lecture series Bart Rienties (Professor of Learning Analytics, head of Academic Professional Development) will discuss how from the safety of your home you could use existing trace data to explore interactions between people (e.g., Twitter data, engagement data in a virtual learning environment, public data sets), and what the affordances and limitations of these trace data might be. Furthermore, he will discuss how other ways of collecting subjective data (e.g., surveys, interviews) might strengthen our understandings of complex interactions between people.
There are no prior requirements to join, and everyone is welcome. For those with a technical background you may enjoy this recent paper in PLOS ONE https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0233977. For those with a non-technical background, you may enjoy this paper https://journals.sfu.ca/flr/index.php/journal/article/view/348
Slides from a practical workshop on gathering customer insights from social media using Social Network Analysis (SNA) with NodeXL and Twitter. SNA allows you to gain insight from thousands of tweets and messages on a range of topics for marketing research or academic use. NodeXL reports can be used for measuring and monitoring an organisation’s own performance as well as a competitors´ performance. At the highest level, a SNA approach allows social media managers to recognize what their audience looks like.
Presentation given at the HEA Social Sciences learning and teaching summit 'Exploring the implications of ‘the era of big data’ for learning and teaching'.
A blog post outlining the issues discussed at the summit is available via: http://bit.ly/1lCBUIB
Activities around digging have again become very popular recently, including in the attention they have received from cultural institutions. Many cultural institutions have in recent years recreated wartime (allotment) gardens to highlight a range of different issues and values. Such exhibitions and events, organized during a time of renewed austerity measures, increased concerns around food and the environment, draw obvious parallels to the contemporary moment, offering possibilities to rethink our own values. This panel brings together exciting new research that focuses on this renewed interest in growing your own food.
The first half of the panel highlights work from the recently completed ‘Everyday Growing Cultures’ project, which focused on the potentially transformative value of connecting two currently disparate communities: allotments growers and the open data community. Based on comparative research in Manchester and Sheffield, it explores potential effects of digital engagement and open data for allotment holders to build stronger, more active communities, benefit local economies and improve environmental sustainability and food security. The second half of the panel seeks to understand the different ways in which issues around digging have reemerged in recent years, to understand these by looking at how they have been expressed and mobilized by different people and actors. This can be expressed as actual digging linked to food production, symbolic digging as performance, digging up local histories, or as new forms of gift-giving.
Panel presentations from: Farida Vis, Ian Humphrey, Yana Manyukhina and Penny Rivlin. Penny's slides will be uploaded separately.
Where do images fit in the era of ‘Big Data’?Farida Vis
This presentation makes an argument for a more central focus on images within social media research. It offers approaches and concrete examples from both 'Big data' and 'small data' perspectives. Presented at the Digital Transformations in the Arts and Humanities: Big Data Workshop, London, June 25 2013.
These are the slides of a keynote I gave at Emerce Eday on 25 October 2012 in Rotterdam.
The short description of my talk was as follows: With the ongoing rise of third party applications like Klout, tools for measuring Twitter influence are important to understand. This presentation takes a look at the different ways in which influence measures have been developed for Twitter. In particular it will use the case study of the UK riots of 2011 for which a database of 2.6 million tweets was collected in collaboration with Twitter and The Guardian newspaper. By examining the top 1000 most tweeted accounts, it will give further insight in how influence worked during this crisis event, specifically highlighting the emergence of the ‘ordinary influential’ during 2011 as well as how large organisations have incorporated social media practices.
Allotment (publics): an open data and data driven journalism perspective Farida Vis
This talk was delivered at the USING OPEN DATA policy modeling, citizen empowerment, data journalism workshop (19-20 June, 2012), organised by the W3C, hosted by the European Commission.
The talk addresses issues of everyday data, related to ‘mundane issues’ that people relate to easily, principally because they feature in their everyday lives. This allows for a rethinking of political participation and civic engagement beyond the rather stale ways in which this is measured traditionally. The paper is interested in ‘really useful’ data, which has the ordinary end user firmly in mind. Specifically it highlights these issues through a case study of allotments in the UK, small plots of land rented from the council to grow fruits and vegetables. This case study highlights larger issues concerning the use and value of open data as well as how data driven journalism can play a role in telling these important stories. It highlight this as an open data case study that could embed open data ideas more firmly in the mainstream and take it outside the world of technology. Having an allotment and growing your own food have become incredibly popular in recent years. Due to a real shortage in allotments, lack of creation of new plots, and ever-growing waiting lists, this research is interested in building on and extending previous work in this area, addressing the following questions: How can allotment data be made really useful?; How can open data go mainstream, securing wide use adoption?
This presentation looks at the ways in which the riots were discussed on Twitter, during the four days of rioting in the UK during the summer of 2011. The 'Reading the Riots on Twitter' project examined 2.6 million riot tweets, focusing specifically on the role of rumours, whether incitement was organised on Twitter as well as who the key users were that tweeted the riots. Finally, it looks at how emergency services in particular can improve their social media strategies in the future.
This presentation looks at alternative ways to gauge how people understand flu pandemics. By looking at what is popular on Amazon.com through searching 'flu pandemics' and 'pandemics' it highlights the meeting of a number of important discourses and the emergence of new ones, specifically within fiction. What are the implications of this?
Multilingual SEO Services | Multilingual Keyword Research | Filosemadisonsmith478075
Multilingual SEO services are essential for businesses aiming to expand their global presence. They involve optimizing a website for search engines in multiple languages, enhancing visibility, and reaching diverse audiences. Filose offers comprehensive multilingual SEO services designed to help businesses optimize their websites for search engines in various languages, enhancing their global reach and market presence. These services ensure that your content is not only translated but also culturally and contextually adapted to resonate with local audiences.
Visit us at -https://www.filose.com/
Unlock TikTok Success with Sociocosmos..SocioCosmos
Discover how Sociocosmos can boost your TikTok presence with real followers and engagement. Achieve your social media goals today!
https://www.sociocosmos.com/product-category/tiktok/
Your Path to YouTube Stardom Starts HereSocioCosmos
Skyrocket your YouTube presence with Sociocosmos' proven methods. Gain real engagement and build a loyal audience. Join us now.
https://www.sociocosmos.com/product-category/youtube/
Grow Your Reddit Community Fast.........SocioCosmos
Sociocosmos helps you gain Reddit followers quickly and easily. Build your community and expand your influence.
https://www.sociocosmos.com/product-category/reddit/
Get Ahead with YouTube Growth Services....SocioCosmos
Get noticed on YouTube by buying authentic engagement. Sociocosmos helps you grow your channel quickly and effectively.
https://www.sociocosmos.com/product-category/youtube/
Exploring Factors Affecting the Success of TVET-Industry Partnership: A Case ...AJHSSR Journal
ABSTRACT: The purpose of this study was to explore factors affecting the success of TVET-industry
partnerships. A case study design of the qualitative research method was used to achieve this objective. For the
study, one polytechnic college of Oromia regional state, and two industries were purposively selected. From the
sample polytechnic college and industries, a total of 17 sample respondents were selected. Out of 17
respondents, 10 respondents were selected using the snowball sampling method, and the rest 7 respondents were
selected using the purposive sampling technique. The qualitative data were collected through an in-depth
interview and document analysis. The data were analyzed using thematic approaches. The findings revealed that
TVET-industry partnerships were found weak. Lack of key stakeholder‟s awareness shortage of improved
training equipment and machines in polytechnic colleges, absence of trainee health insurance policy, lack of
incentive mechanisms for private industries, lack of employer industries involvement in designing and
developing occupational standards, and preparation of curriculum were some of the impediments of TVETindustry partnership. Based on the findings it was recommended that the Oromia TVET bureau in collaboration
with other relevant concerned regional authorities and TVET colleges, set new strategies for creating strong
awareness for industries, companies, and other relevant stakeholders on the purpose and advantages of
implementing successful TVET-industry partnership. Finally, the Oromia regional government in collaboration
with the TVET bureau needs to create policy-supported incentive strategies such as giving occasional privileges
of duty-free import, tax reduction, and regional government recognition awards based on the level of partnership
contribution to TVET institutions in promoting TVET-industry partnership.
KEY WORDS: employability skills, industries, and partnership
How social media marketing helps businesses in 2024.pdfpramodkumar2310
Social media marketing refers to the process of utilizing social media platforms to promote products, services, or brands. It involves creating and sharing valuable content, engaging with followers, analyzing data, and running targeted advertising campaigns.
www.nidmindia.com
Enhance your social media strategy with the best digital marketing agency in Kolkata. This PPT covers 7 essential tips for effective social media marketing, offering practical advice and actionable insights to help you boost engagement, reach your target audience, and grow your online presence.
“To be integrated is to feel secure, to feel connected.” The views and experi...AJHSSR Journal
ABSTRACT: Although a significant amount of literature exists on Morocco's migration policies and their
successes and failures since their implementation in 2014, there is limited research on the integration of subSaharan African children into schools. This paperis part of a Ph.D. research project that aims to fill this gap. It
reports the main findings of a study conducted with migrant children enrolled in two public schools in Rabat,
Morocco, exploring how integration is defined by the children themselves and identifying the obstacles that they
have encountered thus far. The following paper uses an inductive approach and primarily focuses on the
relationships of children with their teachers and peers as a key aspect of integration for students with a migration
background. The study has led to several crucial findings. It emphasizes the significance of speaking Colloquial
Moroccan Arabic (Darija) and being part of a community for effective integration. Moreover, it reveals that the
use of Modern Standard Arabic as the language of instruction in schools is a source of frustration for students,
indicating the need for language policy reform. The study underlines the importanceof considering the
children‟s agency when being integrated into mainstream public schools.
.
KEYWORDS: migration, education, integration, sub-Saharan African children, public school
The Challenges of Good Governance and Project Implementation in Nigeria: A Re...AJHSSR Journal
ABSTRACT : This study reveals that systemic corruption and other factors including poor leadership,
leadership recruitment processes, ethnic and regional politics, tribalism and mediocrity, poor planning, and
variation of project design have been the causative factors that undermine projects implementation in postindependence African states, particularly in Nigeria. The study, thus, argued that successive governments of
African states, using Nigeria as a case study, have been deeply engrossed in this obnoxious practice that has
undermined infrastructure sector development as well as enthroned impoverishment and mass poverty in these
African countries. This study, therefore, is posed to examine the similarities in causative factors, effects and
consequences of corruption and how it affects governance, projects implementation and national growth. To
achieve this, the study adopted historical research design which is qualitative and explorative in nature. The
study among others suggests that the governments of developing countries should shun corruption and other
forms of obnoxious practices in order to operate effective and efficient systems that promote good governance
and ensure there is adequate projects implementation which are the attributes of a responsible government and
good leadership. Policy makers should also prioritize policy objectives and competence to ensure that policies
are fully implemented within stipulated time frame.
KEYWORDS: Developing Countries, Nigeria, Government, Project Implementation, Project Failure
Social media refers to online platforms and tools that enable users to create, share, and exchange information, ideas, and content in virtual communities and networks. These platforms have revolutionized the way people communicate, interact, and consume information. Here are some key aspects and descriptions of social media:
Non-Financial Information and Firm Risk Non-Financial Information and Firm RiskAJHSSR Journal
ABSTRACT: This research aims to examine how ESG disclosure and risk disclosure affect the total risk of
companies. Using cross section data from 355 companies listed in Indonesia Stock Exchange, data regarding
ESG disclosure and risk was collected. In this research, ESG and risk disclosures are measured based on content
analysis using GRI 4 guidelines for ESG disclosures and COSO ERM for risk disclosures. Using multiple
regression, it is concluded that only risk disclosure can reduce the company's total risk, while ESG disclosure
cannot affect the company's total risk. This shows that only risk disclosure is relevant in determining a
company's total risk.
KEYWORDS: ESG disclosure, risk disclosure, firm risk
Non-Financial Information and Firm Risk Non-Financial Information and Firm Risk
The evolution of research on social media
1. The evolution of research on
social media
Farida Vis, University of Sheffield
@flygirltwo
European Conference on Social Media, 10 July, Brighton, United Kingdom
15. 235 posts – 106 individuals
(Flickr)
Aftermath of Hurricane Katrina
2005
Manual collection possible
16. 1413 videos – 700 individuals
(YouTube)
Fitna: The Video Battle
2008
+ Computer Science
17. 2.6 million tweets – 700K individuals
(Twitter)
Reading the Riots on Twitter
2011
+ Lots of Computer Science
18. READING
THE RIOTS
ON TWITTER
Rob Procter (University of Manchester)
Farida Vis (University of Leicester)
Alexander Voss (University of St Andrews)
[Funded by JISC]
#readingtheriots
21. “Big data” is high-volume, -velocity and –variety
information assets that demand cost-effective,
innovative forms of information processing for
enhanced insight and decision making’ (Gartner in
Sicular, 2013).
Huge industry now built around ‘social data’ and
‘listening platforms’ feeding on this data (Many
tools not suitable for academic use, black box).
22. • Technology: maximizing computation power and
algorithmic accuracy to gather, analyze, link, and
compare large data sets.
• Analysis: drawing on large data sets to identify
patterns in order to make economic, social,
technical, and legal claims.
• Mythology: the widespread belief that large data
sets offer a higher form of intelligence and
knowledge that can generate insights that were
previously impossible, with the aura of truth,
objectivity, and accuracy.
(boyd and Crawford p. 663).
23. Critiques of Big Data
• Important to make visible inherent claims about
objectivity
• Problematic focus on quantitative methods
• How can data answer questions it was not
designed to answer?
• How can the right questions be asked?
• Inherent biases in large linked error prone
datasets
• Focus on text and numbers that can be mined
algorithmically
• Data fundamentalism
24. Data fundamentalism
The notion that correlation always indicates causation,
and that massive data sets and predictive analytics
always reflect ‘objective truth’. Idea and belief in the
existence of an objective ‘truth’, that something can be
fully understood from a single perspective, again brings
to light tensions about how the social world can be made
known.
26. How do we ground online data?
In the offline: assessing findings against what we
know about an offline population (census data) in
order to better understand online data. Problems
with over/under representation in online data?
In the online: premised on the idea that data
derived from social media should be grounded in
other online data in order to understand it. So
comparing Facebook use to what we know about
Facebook use, rather than connecting it to offline
measurements about citizens. Richard Rogers
27. Important considerations
1. Asking the right question – research should
be question driven rather than data driven.
2. Accept poor data quality & users gaming
metrics – once online metrics have value users
will try to game them.
3. Limitations of tools (often built in
disconnected way)
4. Transparency – researchers should be upfront
about limitations of research and research
design. Can the data answer the questions?
28. A critical reflection on Big Data:
considering APIs, researchers and
tools as data makers
29. Rather than assuming data already exists ‘out there’, waiting to
simply be recovered and turned into findings, the article
examines how data is co-produced through dynamic research
intersections. A particular focus is the intersections between the
Application Programming Interface (API), the researcher
collecting the data as well as the tools used to process it. In light
of this, the article offers three new ways to define and think
about Big Data and proposes a series of practical suggestions for
making data.
(First Monday, October 2013, http://firstmonday.org/)
31. Standard API sampling problems
Sampling from the FIREHOSE
1% random sample of the firehose
If not rate limited – all data collected?
32. New API sampling problems
New business models: enriched metadata
Social media vs social data
Datasift, GNIP and Topsy
33. Social media VS social data
• Social Media: User-generated content where one user
communicates and expresses themselves and that content
is delivered to other users. Examples of this are platforms
such as Twitter, Facebook, YouTube, Tumblr and Disqus.
Social media is delivered in a great user experience, and is
focused on sharing and content discovery. Social media
also offers both public and private experiences with the
ability to share messages privately.
• Social Data: Expresses social media in a computer-readable
format (e.g. JSON) and shares metadata about the content
to help provide not only content, but context. Metadata
often includes information about location, engagement and
links shared. Unlike social media, social data is focused
strictly on publicly shared experiences. (Cairns, 2013)
39. Geo-locating tweets
Exact location
Lat/long coordinates
Gold standard geo data
Problem: only 1% of users
-> Only 2% of firehose tweets
Early adopters, highly skewed
Where in the world are you?
No Lat/long coordinates
Text field – enter anything
Advantage: more than half of all
tweets contain profile location
Much more evenly distributed
40. Profile Geo Enrichment
‘our customers can now hear from the whole world of Twitter users
and not just 1%’ (Cairs, 2013 on Gnip company Blog)
• Activity Location – 1% that provide lat/long
• Profile Location – Place provided in their profile. May or may not
be posting from there.
• Mentioned Location – Places a user talks about
‘Both the tweet text and Profile fields contain geographic information,
but not in substantial quantities and have poor accuracy’ (Leetaru et
al, First Monday, May 2013)
41. Problem with deleted tweets
‘A deleted tweet effectively disappears from the results of searching
Twitter, although a short delay sometimes occurs between deletion
and disappearance. A status deletion notice is distributed via the
Twitter streaming API to relevant users’ clients so that they, in turn,
remove deleted tweets from their records.’
‘Twitter does not provide a bulk-deletion of user’s tweets. It provides,
however, a one-click bulk-deletion of all location data that were
attached to user’s tweets, without deleting the tweets. By clicking on
the “Delete all location information” button on user’s account settings
page, all locations attached to all previous tweets are deleted.
(Almuhimedi et al, 2013)
42. Profile Geo Enrichment
Linking data
‘Profile location data can be used to unlock demographic data
and other information that is not otherwise possible with
activity location. For instance, US Census Bureau statistics are
aggregated at the locality level and can provide basic stats like
household income. Profile location is also a strong indicator of
activity location when one isn’t provided. (Cairns, 2013)
48. Fake followers: Mitt Romney’s 100,000 extra followers in one day
As many as 20 million fake follower accounts (200 million active users)
This doesn’t take into account the issue of spoof accounts (clearly in
evidence in riot tweets) (Perlroth, 2013)
50. Ability to describe the limitations of our data:
- APIs as data makers. Once data is linked very
hard to untangle how metadata is constructed
and where problems might be. Included in terms
of deleted content.
- Researchers and tools as data makers
51. - When creating a dataset important to
describe how it was made, what the
limitations are. What the sampling limitations
(both in terms of the API, but also related to
offline ‘population’. What other limitations re:
enriched metadata needs to be described?)
- When creating a dataset how complete is it?
- Limitations need to be known in order to
describe them. This is a real problem.
52. Tools as data makers
In answering complex questions about social media
data, we need:
1. Know the questions! And know how they might be
answered.
2. Problem with tools: not question driven. Often
developed around available (poor quality) data, often
by non social media experts, but those with data
processing expertise.
3. Tools therefore become data-makers in that they limit
the scope of possibility in the questions researchers
imagine. This is a huge problem!
67. US 65% smartphone penetration
Smartphones overtaken desktop usage to access the internet
Mobile internet accounts for majority of internet use in US (57%)
Users typically access the internet via apps on mobile devices
All figures from comScore, US Digital Future in Focus, 2014
68. UK: The over-55s will experience the fastest year-on-year rises in
smartphone penetration.
Smartphone ownership should increase to about 50% by year-end, a
25% increase from 2013, but trailing 70% penetration among 18-54s.
The difference in smartphone penetration by age will disappear, but
differences in usage of smartphones remain substantial. Many over
55s use smartphones like feature phones.
All figures from Deloitte, predictions for 2014
69. Rise of platforms and apps focused on visual content
Pinterest
Tumblr
Instagram
Vine
Snapchat
‘Mobile first… and only’ | simple easy, user friendly design
70. Facebook daily image uploads: 350 million (November 2013)
Instagram daily image uploads: 60 million (March 2014)
Twitter: 500 million tweets daily (March 2014)
Snapchat daily snaps: 400 million (November 2013)
71.
72. Images largely ignored in
social media research
Not easy to ‘mine’
Hard to figure out meaning
Huge interest in industry
76. References
• Hazim Almuhimedia, Shomir Wilsona, Bin Liua, Norman Sadeha, Alessandro Acquistib, 2012. ‘Tweets Are
Forever: A Large-Scale Quantitative Analysis of Deleted Tweets’, CSCW’13, February 23–27, 2013, San
Antonio, Texas, USA, http://www.cs.cmu.edu/~shomir/cscw2013_tweets_are_forever.pdf, accessed 18
September, 2013.
• Ian Cairns, 2013. ‘Get More Geodata From Gnip With Our New Profile Geo Enrichment’, Gnip Company
Blog, 22 August, at http://blog.gnip.com/tag/geolocation/, accessed 13 September 2013.
• Grcommunication, 2012, ‘I will help raise your Klout score by sending you 10Ks and will tweet it out to my
50K+ followers from my 80+ Klout score for $5, http://fiverr.com/grcommunication/help-raise-your-klout-
score-by-sending-you-10ks-and-will-tweet-it-out-to-my-17k-followers-from-my-70-klout-score, accessed
19 September 2013.
• Anthony Ha, 2013. ‘Gnip Expands Its Partnership With Klout, Becoming The Exclusive Provider Of
Klout Topics’, TechCrunch, 8 August, http://techcrunch.com/2013/08/08/gnip-klout/, accessed 19
September 2013.
• Martin Hawksey, 2013. ‘Twitter throws a bone: Increased hits and metadata in Twitter Search API 1.1,’
March 28, at http://mashe.hawksey.info/2013/03/twitterthrows-a-bone-increased-hits-and-metadata-in-
twitter-search-api-1-1/ , accessed 10 September 2013.
• Kalev H. Leetaru, Shaowen Wang, Guofeng Cao, Anand Padmanabhan, and Eric Shook, 2013, ‘Mapping the
global Twitter heartbeat: The geography of Twitter, First Monday, Volume 18, Number 5-6 May,
http://firstmonday.org/article/view/4366/3654
• Nicole Perlroth, 2013. ‘Fake Twitter Followers Become Multimillion-Dollar Business’, New York Times, Bits
blog, 5 April, http://bits.blogs.nytimes.com/2013/04/05/fake-twitter-followers-becomes-multimillion-
dollar-business/, accessed 19 September 2013.
• Farida Vis, 2013. ‘A critical reflection on Big Data: considering APIs, researchers and tools as data makers’,
First Monday, 7 October, http://firstmonday.org