The document summarizes Xiaoju Zheng's dissertation proposal defense on studying the life cycle of hashtags on Twitter. It discusses research questions about how words are created on Twitter using hashtags, what makes some hashtags more successful than others, and how successful hashtags spread. It proposes analyzing hashtag data linguistically and using diffusion models to examine social factors like network structure that influence a hashtag's popularity. Challenges include developing hashtag classification metrics and finding representative data.
Tracking the Emergence of New Words across Time and SpaceDigital History
This document discusses tracking the emergence and spread of new words across time and space using a large Twitter corpus. It identifies rising and emerging words from 2014 using correlation analysis and cross-referencing with rare words. Many emerging words follow an S-curve pattern of increasing usage over time. Mapping analyses show words tend to spread from urban to surrounding areas, though factors like population density and demographics also influence patterns of geographical diffusion.
Handling and Mining Linguistic Variation in UGCLeon Derczynski
This document discusses user-generated content (UGC) found on social media and the linguistic variation present within it. It notes that UGC comes directly from end users without editing and contains nonstandard spelling, grammar, slang, and abbreviations. The document qualitatively and quantitatively analyzes the nature of this variation, including its relationship to social factors. It also discusses challenges this variation poses for natural language processing systems and different approaches that have been explored to better handle UGC, such as distributional semantic models, normalization, and leveraging author metadata.
Broad Twitter Corpus: A Diverse Named Entity Recognition ResourceLeon Derczynski
This presents a new resource for helping to find names of entities in social media. It takes an inclusive approach, meaning we get high variety in named entities - something other corpora have struggled with, leaving them poorly placed to help machine learning approaches generalise beyond the lexical level.
1) The document discusses the use and popularity of hashtags on social media platforms like Twitter. It explores how hashtags are used to group tweets by topic or event and how some hashtags gain widespread usage while others remain niche.
2) Predicting which hashtags will become widely popular is important for economic and marketing reasons. Factors like how the hashtag is used by influential accounts and how it spreads through social connections can impact its adoption.
3) The hashtag symbol was originally used on IRC to label groups and topics but became mainstream on Twitter, where clicking on a hashtag links to other tweets using it. General interest hashtags like #lol tend to be more popular than specific ones.
1. The document analyzes word-of-mouth sharing of URLs on Twitter to understand how users discover content through social media.
2. It finds that word-of-mouth on Twitter can spread a single URL to a large audience, in some cases reaching millions of users, through multiple propagation trees on the social network.
3. The analysis aims to understand questions like how far word-of-mouth can spread content, what types of content are popular on social media versus the wider web, and typical structures of word-of-mouth propagation trees.
Slides from a Brighttalk given 09-10-15. Problem-first thinking, what's actually exciting about Big Data, and how to get there.
Video is here: https://www.brighttalk.com/webcast/9059/169665
Twitter is a social media platform that allows users to post short messages called tweets. Many businesses and individuals are using Twitter to connect with customers, generate leads, and drive traffic to their websites. The document discusses how to effectively use Twitter for business purposes by being authentic and engaging with others on the platform.
Tracking the Emergence of New Words across Time and SpaceDigital History
This document discusses tracking the emergence and spread of new words across time and space using a large Twitter corpus. It identifies rising and emerging words from 2014 using correlation analysis and cross-referencing with rare words. Many emerging words follow an S-curve pattern of increasing usage over time. Mapping analyses show words tend to spread from urban to surrounding areas, though factors like population density and demographics also influence patterns of geographical diffusion.
Handling and Mining Linguistic Variation in UGCLeon Derczynski
This document discusses user-generated content (UGC) found on social media and the linguistic variation present within it. It notes that UGC comes directly from end users without editing and contains nonstandard spelling, grammar, slang, and abbreviations. The document qualitatively and quantitatively analyzes the nature of this variation, including its relationship to social factors. It also discusses challenges this variation poses for natural language processing systems and different approaches that have been explored to better handle UGC, such as distributional semantic models, normalization, and leveraging author metadata.
Broad Twitter Corpus: A Diverse Named Entity Recognition ResourceLeon Derczynski
This presents a new resource for helping to find names of entities in social media. It takes an inclusive approach, meaning we get high variety in named entities - something other corpora have struggled with, leaving them poorly placed to help machine learning approaches generalise beyond the lexical level.
1) The document discusses the use and popularity of hashtags on social media platforms like Twitter. It explores how hashtags are used to group tweets by topic or event and how some hashtags gain widespread usage while others remain niche.
2) Predicting which hashtags will become widely popular is important for economic and marketing reasons. Factors like how the hashtag is used by influential accounts and how it spreads through social connections can impact its adoption.
3) The hashtag symbol was originally used on IRC to label groups and topics but became mainstream on Twitter, where clicking on a hashtag links to other tweets using it. General interest hashtags like #lol tend to be more popular than specific ones.
1. The document analyzes word-of-mouth sharing of URLs on Twitter to understand how users discover content through social media.
2. It finds that word-of-mouth on Twitter can spread a single URL to a large audience, in some cases reaching millions of users, through multiple propagation trees on the social network.
3. The analysis aims to understand questions like how far word-of-mouth can spread content, what types of content are popular on social media versus the wider web, and typical structures of word-of-mouth propagation trees.
Slides from a Brighttalk given 09-10-15. Problem-first thinking, what's actually exciting about Big Data, and how to get there.
Video is here: https://www.brighttalk.com/webcast/9059/169665
Twitter is a social media platform that allows users to post short messages called tweets. Many businesses and individuals are using Twitter to connect with customers, generate leads, and drive traffic to their websites. The document discusses how to effectively use Twitter for business purposes by being authentic and engaging with others on the platform.
Twitter is a microblogging platform that allows users to post short messages called tweets that are limited to 140 characters. Key Twitter terms include tweets, retweets, handles, hashtags, and direct messages. Users can follow other accounts and see their tweets in their Twitter feed. Engaging with other users through replies, mentions, and retweets can help one build their own following on Twitter. Hashtags help group tweets by topic to facilitate finding relevant conversations.
Will Twitter change the way that market researchers communicate?Daniel Alexander-Head
Most conference papers and presentations tend to focus on one of the following: users/buyers of products and services, brands, or methodology. This paper, by contrast, looks at market researchers themselves and asks whether social media in general and Twitter in particular are changing the way that researchers communicate with each other. The paper is complemented by an interactive event held at the ESOMAR APAC Conference in Bangkok (April 2010).
The paper starts by providing some background information on Twitter, before moving on to explore the
ways that market researchers are beginning to utilise Twitter, both as medium for research and as a method of opening up new and exciting channels (and back-channels) amongst researchers.
The paper includes four in-depth reviews of the impact of Twitter in Australia, China, Japan and New Zealand. Finally, the paper draws the threads together in an overall summary and list of key
recommendations.
Slides for ssm presentation. Catherine Boothcbooth123
1) The document discusses how hashtags on social media, particularly Twitter, are used to group messages and topics. By including a hashtag, users can connect their tweets to others on the same topic.
2) It provides the example of the destructive #NekNominate challenge in 2014 and how a response video with the hashtag #ChangeOneThing was able to redirect the conversation.
3) Predicting which hashtags will become popular on Twitter is important economically due to the platform's influence. Hashtags are analyzed based on their spread through users' social connections and semantic similarities to other tags.
This document summarizes a presentation on terminology trends from a blogger's perspective. It discusses how language lovers use social networks like blogs, Facebook, and Twitter to communicate about terminology by researching, asking questions, answering questions of followers, reporting on conferences, and providing helpful tips, news, and job opportunities. Social networks produce large amounts of text data that can be used for terminology research to analyze evolving language and identify neologisms. Tools like the Global Language Monitor use natural language processing of social media to track new terms and their usage in real-time.
This paper analyzes social media conversations around the TomorrowWorld music festival through two Twitter data sets collected a month apart. It finds that the first data set focused mainly on performances from this year's festival, while the second shifted to next year's event. The paper also examines the Twitter account @belugaPOD and recommends increasing interactions with important users and involvement in smaller conversations to improve their presence. Google Analytics showed most important website visitors came from SoundCloud. Overall, the paper aims to understand social media discussions of TomorrowWorld and how to enhance @belugaPOD's online and social media presence.
Metaphic or the art of looking another way.Suresh Manian
For all intents and purposes, we are our words. And verbs and adjectives capture actions and sentiments better than any other tool. Metaphic is premised on the belief that a grammar book and a calculator are all you really need to make sense of web search and social media chatter, apart from all text, in general.
A really concise and action oriented guide to using twitter more effectively targeted at novice users, particularly media folk by someone who has done A LOT of twitter training.
This document discusses incentive-based tagging to maximize the quality of resource tagging in social tagging systems. It finds that most resources are under-tagged, receiving too few tags to be useful, while a few popular resources are over-tagged. It proposes rewarding users for tagging under-tagged resources to address this imbalance. The key concepts of tagging quality, tagging stability, unstable and stable points are introduced. An optimal incentive allocation algorithm is presented to decide how to distribute rewards among resources to maximize overall tagging stability.
Handout from Monica's session.
Tweet, tweet. Have you heard about Twitter all over the media, but still aren’t sure how it works or what it can do to help your organization? Then this session is for you! For nonprofits, Twitter is a versatile tool in your emerging social media toolkit to help tell your story, build your brand and increase stewardship among supporters. Participants will learn about:
- Getting started on Twitter
- How to build and keep a list of followers
- Twitter etiquette
- Case studies and success stories – how other charities and non-profits are benefiting from Twitter
This document discusses ideas for studying information diffusion on online social networks like Twitter. It provides background on lexical diffusion and how words can change pronunciation gradually over time. Prior research has found that high frequency words tend to change faster than low frequency words. The document also discusses how social networks can influence information spread, with central and well-connected users playing an important role. Methods are proposed for collecting Twitter data through its public API and crawling the network in a constrained way to study topics that spread through hashtags and retweets.
This document provides an introduction and overview of the social media platform Twitter. It explains that Twitter allows users to post short text updates called tweets that are visible to their followers. The document discusses why Twitter is important as an archive of public conversations and how both individuals and brands can use it to share content, start discussions, build communities and conduct research. It also provides basic instructions for setting up a Twitter account and engaging with others on the platform through replies, retweets and hashtags.
1) The document discusses the use and popularity of hashtags on social media platforms like Twitter. It explores how hashtags are used to group related tweets on a topic and how some hashtags gain widespread usage while others remain niche.
2) The popularity of hashtags can be predicted based on how they spread through social connections on platforms like Twitter. Newly created hashtags that are used to discuss emerging topics or events have the potential to take off if influential users help spread them.
3) The origins of the hashtag can be traced back to IRC chat, but it was Twitter that helped popularize its use by making hashtags clickable links to view other tweets containing that tag. Now hashtags are an essential way
In this talk, Dr. Jeanne Bohannon parses out how and why hashtags can be used rhetorically on Twitter. Please cite and credit if you use this slideshare.
The document discusses the current state of new media and online literacy. It notes that more people are using social media and participating in user-generated content like blogs. New media allows for personalization, interaction, content creation and collaboration between users. Stories online can be interactive and involve the reader directly, take many forms beyond just text, and can be both individually authored and collaboratively written. Folksonomies and tagging allow users to organize content in personalized ways and say something about how individuals categorize information.
This document provides an introduction to Twitter and how it can be useful for academics and researchers. It explains that Twitter is a social media platform that allows users to share messages up to 140 characters. For researchers, Twitter can be used to build scholarly networks, stay up to date in their field, share resources, and engage with others who have similar interests. Several researchers explain how Twitter has benefited them professionally by helping make connections, get feedback on ideas, and find relevant resources. The document concludes by offering tips for using Twitter effectively such as asking questions, maintaining a balanced professional and personal presence, and telling others about your Twitter account.
Slides for ssm presentation Catherine Boothcbooth123
1) The document discusses how hashtags on social media, particularly Twitter, are used to group messages and topics. By including a hashtag, users can connect their tweets to others on the same topic.
2) It also examines how influential individuals and popular hashtags spread ideas through social networks on these platforms. A case study is presented on the #NekNominate hashtag and how it was used to both spread a dangerous challenge but also draw attention to promoting positive change.
3) Predicting which hashtags will become widely popular is important for businesses and organizations to understand how to effectively engage users. A campaign example is given for the #milk4kids hashtag that was very successful at raising awareness and funds
If you're into campaigning or direct action this training presentation offer handy tips on understanding what Twitter is, how it works, how to build a community of followers as well as examining a case study of its use in direct action
This document summarizes research on characterizing content on the social media platform Twitter using topic modeling techniques. The researchers apply a partially supervised topic modeling method called Labeled LDA to tweets in order to discover latent topics as well as topics related to hashtags, replies, and other labeled elements. They find that the topic models are able to characterize users and tweets according to substance, style, status, and social dimensions. The researchers also conducted surveys to better understand how and why users follow others on Twitter in order to identify important information needs that current Twitter interfaces fail to support.
Tags, Networks, Narrative: Investigating the Use of Social Software for the S...Bruce Mason
Presented at "Towards a Social Science of Web 2.0", University of York, 6 September 2007. This presentation reported on the project findings to a mixed audience of academics and industry specialists.
Twitter is a microblogging platform that allows users to post short messages called tweets that are limited to 140 characters. Key Twitter terms include tweets, retweets, handles, hashtags, and direct messages. Users can follow other accounts and see their tweets in their Twitter feed. Engaging with other users through replies, mentions, and retweets can help one build their own following on Twitter. Hashtags help group tweets by topic to facilitate finding relevant conversations.
Will Twitter change the way that market researchers communicate?Daniel Alexander-Head
Most conference papers and presentations tend to focus on one of the following: users/buyers of products and services, brands, or methodology. This paper, by contrast, looks at market researchers themselves and asks whether social media in general and Twitter in particular are changing the way that researchers communicate with each other. The paper is complemented by an interactive event held at the ESOMAR APAC Conference in Bangkok (April 2010).
The paper starts by providing some background information on Twitter, before moving on to explore the
ways that market researchers are beginning to utilise Twitter, both as medium for research and as a method of opening up new and exciting channels (and back-channels) amongst researchers.
The paper includes four in-depth reviews of the impact of Twitter in Australia, China, Japan and New Zealand. Finally, the paper draws the threads together in an overall summary and list of key
recommendations.
Slides for ssm presentation. Catherine Boothcbooth123
1) The document discusses how hashtags on social media, particularly Twitter, are used to group messages and topics. By including a hashtag, users can connect their tweets to others on the same topic.
2) It provides the example of the destructive #NekNominate challenge in 2014 and how a response video with the hashtag #ChangeOneThing was able to redirect the conversation.
3) Predicting which hashtags will become popular on Twitter is important economically due to the platform's influence. Hashtags are analyzed based on their spread through users' social connections and semantic similarities to other tags.
This document summarizes a presentation on terminology trends from a blogger's perspective. It discusses how language lovers use social networks like blogs, Facebook, and Twitter to communicate about terminology by researching, asking questions, answering questions of followers, reporting on conferences, and providing helpful tips, news, and job opportunities. Social networks produce large amounts of text data that can be used for terminology research to analyze evolving language and identify neologisms. Tools like the Global Language Monitor use natural language processing of social media to track new terms and their usage in real-time.
This paper analyzes social media conversations around the TomorrowWorld music festival through two Twitter data sets collected a month apart. It finds that the first data set focused mainly on performances from this year's festival, while the second shifted to next year's event. The paper also examines the Twitter account @belugaPOD and recommends increasing interactions with important users and involvement in smaller conversations to improve their presence. Google Analytics showed most important website visitors came from SoundCloud. Overall, the paper aims to understand social media discussions of TomorrowWorld and how to enhance @belugaPOD's online and social media presence.
Metaphic or the art of looking another way.Suresh Manian
For all intents and purposes, we are our words. And verbs and adjectives capture actions and sentiments better than any other tool. Metaphic is premised on the belief that a grammar book and a calculator are all you really need to make sense of web search and social media chatter, apart from all text, in general.
A really concise and action oriented guide to using twitter more effectively targeted at novice users, particularly media folk by someone who has done A LOT of twitter training.
This document discusses incentive-based tagging to maximize the quality of resource tagging in social tagging systems. It finds that most resources are under-tagged, receiving too few tags to be useful, while a few popular resources are over-tagged. It proposes rewarding users for tagging under-tagged resources to address this imbalance. The key concepts of tagging quality, tagging stability, unstable and stable points are introduced. An optimal incentive allocation algorithm is presented to decide how to distribute rewards among resources to maximize overall tagging stability.
Handout from Monica's session.
Tweet, tweet. Have you heard about Twitter all over the media, but still aren’t sure how it works or what it can do to help your organization? Then this session is for you! For nonprofits, Twitter is a versatile tool in your emerging social media toolkit to help tell your story, build your brand and increase stewardship among supporters. Participants will learn about:
- Getting started on Twitter
- How to build and keep a list of followers
- Twitter etiquette
- Case studies and success stories – how other charities and non-profits are benefiting from Twitter
This document discusses ideas for studying information diffusion on online social networks like Twitter. It provides background on lexical diffusion and how words can change pronunciation gradually over time. Prior research has found that high frequency words tend to change faster than low frequency words. The document also discusses how social networks can influence information spread, with central and well-connected users playing an important role. Methods are proposed for collecting Twitter data through its public API and crawling the network in a constrained way to study topics that spread through hashtags and retweets.
This document provides an introduction and overview of the social media platform Twitter. It explains that Twitter allows users to post short text updates called tweets that are visible to their followers. The document discusses why Twitter is important as an archive of public conversations and how both individuals and brands can use it to share content, start discussions, build communities and conduct research. It also provides basic instructions for setting up a Twitter account and engaging with others on the platform through replies, retweets and hashtags.
1) The document discusses the use and popularity of hashtags on social media platforms like Twitter. It explores how hashtags are used to group related tweets on a topic and how some hashtags gain widespread usage while others remain niche.
2) The popularity of hashtags can be predicted based on how they spread through social connections on platforms like Twitter. Newly created hashtags that are used to discuss emerging topics or events have the potential to take off if influential users help spread them.
3) The origins of the hashtag can be traced back to IRC chat, but it was Twitter that helped popularize its use by making hashtags clickable links to view other tweets containing that tag. Now hashtags are an essential way
In this talk, Dr. Jeanne Bohannon parses out how and why hashtags can be used rhetorically on Twitter. Please cite and credit if you use this slideshare.
The document discusses the current state of new media and online literacy. It notes that more people are using social media and participating in user-generated content like blogs. New media allows for personalization, interaction, content creation and collaboration between users. Stories online can be interactive and involve the reader directly, take many forms beyond just text, and can be both individually authored and collaboratively written. Folksonomies and tagging allow users to organize content in personalized ways and say something about how individuals categorize information.
This document provides an introduction to Twitter and how it can be useful for academics and researchers. It explains that Twitter is a social media platform that allows users to share messages up to 140 characters. For researchers, Twitter can be used to build scholarly networks, stay up to date in their field, share resources, and engage with others who have similar interests. Several researchers explain how Twitter has benefited them professionally by helping make connections, get feedback on ideas, and find relevant resources. The document concludes by offering tips for using Twitter effectively such as asking questions, maintaining a balanced professional and personal presence, and telling others about your Twitter account.
Slides for ssm presentation Catherine Boothcbooth123
1) The document discusses how hashtags on social media, particularly Twitter, are used to group messages and topics. By including a hashtag, users can connect their tweets to others on the same topic.
2) It also examines how influential individuals and popular hashtags spread ideas through social networks on these platforms. A case study is presented on the #NekNominate hashtag and how it was used to both spread a dangerous challenge but also draw attention to promoting positive change.
3) Predicting which hashtags will become widely popular is important for businesses and organizations to understand how to effectively engage users. A campaign example is given for the #milk4kids hashtag that was very successful at raising awareness and funds
If you're into campaigning or direct action this training presentation offer handy tips on understanding what Twitter is, how it works, how to build a community of followers as well as examining a case study of its use in direct action
This document summarizes research on characterizing content on the social media platform Twitter using topic modeling techniques. The researchers apply a partially supervised topic modeling method called Labeled LDA to tweets in order to discover latent topics as well as topics related to hashtags, replies, and other labeled elements. They find that the topic models are able to characterize users and tweets according to substance, style, status, and social dimensions. The researchers also conducted surveys to better understand how and why users follow others on Twitter in order to identify important information needs that current Twitter interfaces fail to support.
Tags, Networks, Narrative: Investigating the Use of Social Software for the S...Bruce Mason
Presented at "Towards a Social Science of Web 2.0", University of York, 6 September 2007. This presentation reported on the project findings to a mixed audience of academics and industry specialists.
Social Media Posts On Platforms Such As Twitter Or Instagram Use Hashtags,Which Are Author-Created
Labels Representing Topics Or Themes, Toassist In Categorization Of Posts And Searches For Posts Of
Interest. The Structural Analysis Of Hashtags Is Necessary As Precursor To Understandingtheir Meanings.
This Paper Describes Our Work On Segmenting Nondelimited Strings Of Hashtag-Type English Text. We
Adapt And Extend Methods Used Mostly In Non-Eng
Use and Applications of Social Media in ResearchHarris Lygidakis
This is a presentation about the Use and Applications of Social Media in Medical Research.
A big thanks to the #hcsmanz community and all the Twitter and Social Media users that made this presentation possible by providing valuable material.
Investigating the Use of Social Software for the Study of Narrative Digital C...Bruce Mason
1. The document summarizes a study investigating the use of social software tools for organizing and sharing information about online narratives.
2. Researchers surveyed existing collaborative tagging and social networking tools, and studied how groups tag websites to classify information.
3. The study found that user-generated tagging produced more tags than formal taxonomy, and tagging behaviors varied from narrow to broad. Further research on effective use of these tools for collaboration is planned.
Unfortunately, healthcare isn’t as simple as baseball. And to make matters worse, we all know how healthcare loves to use abbreviations, technical jargon, and even use different terminology that essentially all means the same thing. Now lay on top of that the fact that Twitter hashtags are home-grown, without any rules, and without informing the rest of the healthcare community on Twitter what exactly your chosen hashtag means. Take all these issues, stir them up in a pot and healthcare hashtags often become more like mishmash-tags.
This document summarizes Stephen Abram's presentation on social institutions and the social web. In 3 sentences: Abram discusses how libraries can leverage social tools like web 2.0 to better engage patrons and focus on questions rather than books/transactions; he advocates building knowledge portals around common questions and emphasizing quality content over quantity; and recommends that libraries measure impact and value through strategic analytics to demonstrate their continued relevance in a digital age.
NASW Workshop: The Secret Life of Social MediaDennis Meredith
What you think you know about social media is probably wrong. This session will discuss how these tools actually operate, often at odds with promoted functions. Based on data collected and analyzed by panelists and online science publications, we will discuss Digg, reddit, StumbleUpon, Slashdot, Facebook, Twitter, and other social media tools (with background materials for the uninitiated).
The Tenure Track Dream Team presentation by Ines Mergel: "Why academics should tweet and blog too!", 10/08/2010 for PhD students and Postdocs at Syracuse University's Future Professorial Program, SU's Graduate Career Center and Graduate School
This document discusses using Twitter for business and marketing purposes. It provides an overview of Twitter, including its history and usage statistics. Various Twitter features are explained like hashtags, mentions, and retweets. Advice is given on how businesses can use Twitter to generate leads, drive traffic, build relationships and monitor industry trends. Examples are given of both established and emerging brands that use Twitter successfully.
Full-RAG: A modern architecture for hyper-personalizationZilliz
Mike Del Balso, CEO & Co-Founder at Tecton, presents "Full RAG," a novel approach to AI recommendation systems, aiming to push beyond the limitations of traditional models through a deep integration of contextual insights and real-time data, leveraging the Retrieval-Augmented Generation architecture. This talk will outline Full RAG's potential to significantly enhance personalization, address engineering challenges such as data management and model training, and introduce data enrichment with reranking as a key solution. Attendees will gain crucial insights into the importance of hyperpersonalization in AI, the capabilities of Full RAG for advanced personalization, and strategies for managing complex data integrations for deploying cutting-edge AI solutions.
GraphRAG for Life Science to increase LLM accuracyTomaz Bratanic
GraphRAG for life science domain, where you retriever information from biomedical knowledge graphs using LLMs to increase the accuracy and performance of generated answers
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
OpenID AuthZEN Interop Read Out - AuthorizationDavid Brossard
During Identiverse 2024 and EIC 2024, members of the OpenID AuthZEN WG got together and demoed their authorization endpoints conforming to the AuthZEN API
Your One-Stop Shop for Python Success: Top 10 US Python Development Providersakankshawande
Simplify your search for a reliable Python development partner! This list presents the top 10 trusted US providers offering comprehensive Python development services, ensuring your project's success from conception to completion.
Generating privacy-protected synthetic data using Secludy and MilvusZilliz
During this demo, the founders of Secludy will demonstrate how their system utilizes Milvus to store and manipulate embeddings for generating privacy-protected synthetic data. Their approach not only maintains the confidentiality of the original data but also enhances the utility and scalability of LLMs under privacy constraints. Attendees, including machine learning engineers, data scientists, and data managers, will witness first-hand how Secludy's integration with Milvus empowers organizations to harness the power of LLMs securely and efficiently.
Climate Impact of Software Testing at Nordic Testing DaysKari Kakkonen
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
Have you ever been confused by the myriad of choices offered by AWS for hosting a website or an API?
Lambda, Elastic Beanstalk, Lightsail, Amplify, S3 (and more!) can each host websites + APIs. But which one should we choose?
Which one is cheapest? Which one is fastest? Which one will scale to meet our needs?
Join me in this session as we dive into each AWS hosting service to determine which one is best for your scenario and explain why!
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
Building Production Ready Search Pipelines with Spark and MilvusZilliz
Spark is the widely used ETL tool for processing, indexing and ingesting data to serving stack for search. Milvus is the production-ready open-source vector database. In this talk we will show how to use Spark to process unstructured data to extract vector representations, and push the vectors to Milvus vector database for search serving.
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slackshyamraj55
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
Best 20 SEO Techniques To Improve Website Visibility In SERPPixlogix Infotech
Boost your website's visibility with proven SEO techniques! Our latest blog dives into essential strategies to enhance your online presence, increase traffic, and rank higher on search engines. From keyword optimization to quality content creation, learn how to make your site stand out in the crowded digital landscape. Discover actionable tips and expert insights to elevate your SEO game.
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
Ivanti’s Patch Tuesday breakdown goes beyond patching your applications and brings you the intelligence and guidance needed to prioritize where to focus your attention first. Catch early analysis on our Ivanti blog, then join industry expert Chris Goettl for the Patch Tuesday Webinar Event. There we’ll do a deep dive into each of the bulletins and give guidance on the risks associated with the newly-identified vulnerabilities.
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
Ocean lotus Threat actors project by John Sitima 2024 (1).pptxSitimaJohn
Ocean Lotus cyber threat actors represent a sophisticated, persistent, and politically motivated group that poses a significant risk to organizations and individuals in the Southeast Asian region. Their continuous evolution and adaptability underscore the need for robust cybersecurity measures and international cooperation to identify and mitigate the threats posed by such advanced persistent threat groups.
2. Road Map of the Presentation Background of research questions Research Questions Overview of the data Follow-up Experiments Diffusion models Challenges 2
3. The Laws of Imitation “why, given one hundred different innovations conceived of at the same time – innovations in the form of words, in mythological ideas, in industrial processes, etc. – ten will spread abroad, while ninety will be forgotten.” --- Gabriel Tarde (1903) “The Laws of Imitation” Merit of its own? Or something else? 3
4. The Laws of Imitation “……..we see that the incessant struggle between minor linguistic inventions which always ends in the imitation of one of them, and in the abortion of the others, finally comes to transform a language in such a way as to adapt it, more or less rapidly and completely, according to the spirit of the community, to external realities and to the social purposes of language. ..” --- Gabriel Tarde (1903) “The Laws of Imitation” 4
5. The Laws of Imitation Merit is not the only catalyst of the spread of an idea. In situations where “the poorest innovations, from the point of view of logic, are selected because of their place, or even date of birth.”, Tarde attributes these irrational occurrences to “extra-logical influences” 5
6. Social Network Analysis Current research in social network analysis asserts that these “extra-logical” influences can be explained by examining the dynamics of the network through which influence is transmitted between individuals. In other words, if we view individuals as nodes in a social network, where a directed edge indicates that one node influences another, then some graph configurations make it more likely that an innovation will be widely adopted than others. 6
7. Basic Research Questions How is a word created? What makes a newly-created word better than others? How is a newly-created word picked up by users at large? How does a word gain popularity among the population? In a word, the life cycle of a word. 7
8. Research Question --- Data Word creation in EnglishIn spoken English, it can take decades – even centuries – for new words to emerge, become part of common parlance, and then fade into disuse. Word creation on Twitter,a word in the form of #hashtags can live the entire lifecycle in very short period of time, e.g. a couple of days A news story breaks, and competing hashtags vie for dominance. Then a few influential people adopt the same one. Suddenly the conversation coalesces around it, the term trends, the spammers start using it, and then the conversation peters out as we move on to the next topic. (only one possibility) Is that the pattern? And how closely does it map onto the ways that words and phrases emerge in spoken language? #hashtag – word on twitter 8
9. Twitter Twitter.com:Twitter is a social networking and micro-blogging service that enables its users to send and read messages known as tweets. Tweets are text-based posts of up to 140 characters displayed on the author's profile page and delivered to the author's subscribers who are known as followers. 9
10. Twitter: some conventions 10 @mentions - following word is the name of a twitter user and as such this tweet refers to that user, e.g. ”@dave thanks for the help” or ”Talking with @paul about twitter”. (can be used to spot smaller network) Retweets -”RT” means ”I am retweeting (copying) something from elsewhere”, e.g. ”RT@john I just saw Madonna” means that I am retweeting theoriginal message from John (can be used to spot smaller network) #hashtags –give contextual relevance to a tweet or identified as a keyword, e.g. ”Like this demo #acita09” or ”Why does #ms-word keep crashing”
11. Top Influential People on Twitter the Edinburgh Twitter Corpus (around 2 billion tokens) 11 Six Singers
12. Top 10 trending topic from the Edinburgh Twitter Corpus (around 2 billion tokens) 12
14. Research Question Word creation and its propsperitywhat count as criteria for a newly-coined “word” to be accepted as a good #hashtag and how a good #hashtag gain popularity among groups of people. Logical: linguistic groundings of a good #hashtagLinguistic analysis of the #hashtags and behavioral studies Extra-logical: social groundings of a popular #hashtage.g. network structure and dynamics 14
16. I. Linguistic Analysis of #hashtagsmore at https://docs.google.com/Doc?docid=0AWbvIzcQLhQXZGdoY256cDJfMTExZjZjbjNxbTM&hl=en Linking words into a sentence: e.g. whatsyourbackground, tweetwhatyoueatLetsMakeATrendingTopic,goodluckjustin Part of word + existing word: e.g. animtip,appstore Compounding: noun + noun e.g. sundayhug, pubquiz, waikikilunch Compounding: verb + noun e.g. hashtagme, pickon,killcapscop Compounding: adv + verb e.g. currentlycrushing Compounding: adj + noun e.g. digitalbritan, GoodTimes, morningsickness Splinter: e.g. socialmem (SocialCamp Memphis), Acronym & Initials: e.g. smlb (St Michael Le Belfrey church), emr (electronic medical records), #eu (European Union), #cah (Crimes against humanity) Neologism (splinter involved): e.g. twacker(twitter users who lose user account), tweetie (Twitter client for Mac and iPhone), twitvorce(to divorce yourself from a Twitter member by unfollowing them), twittertopia, twendsetter MISC: omgfact, #tcot (top conversation on twitter) 16
17. Preliminary Analysis Public timeline: 20 tweets per minute 20 days of non-stop crawling Total tweets = 567,091 Total words = 8,495,323 Average words per tweet = 14.98 NPS Chat Corpus: 45010 tokens/6,066 types Webtext corpus in NLTK: 396,736 tokens/21,537 types 17
21. Twitter presents a different genre of texts Self expression: "I" is the top-ranking word that tweets begin with. Stats update: "Watching", "trying", "listening", "reading" and "eating" are all in the Top 100 first words, revealing just how often people use Twitter to report on whatever they are experiencing at the time. News broadcast: The abbreviation "RT" (retweet) is extremely common 21
22. Twitter presents a different genre of text popular web addresses (e.g. URL shortening service) among the top 500: "tinyurl.com", "twitpic.com", "ff.im", "twurl.nl". These all appear because they offer services useful to twitterers. Tech vocabulary: among top 500: "Google”, “Faceobok”:, “internet”, “website”, “blog”, “Mac”, and “app”. popular web addresses (e.g. URL shortening service) among the top 500: "tinyurl.com", "twitpic.com", "ff.im", "twurl.nl". These all appear because they offer services useful to twitterers. Tech vocabulary: among top 500: "Google”, “Faceobok”:, “internet”, “website”, “blog”, “Mac”, and “app”. 22
23. Research Question 23 linguistic groundings of a good #hashtagLinguistic analysis of the #hashtags and behavioral studies social groundings of a popular #hashtage.g. network structure and dynamics Would linguistically equally good #hashtags have different degrees of popularity? Is it because of the different network structure? Behavioral studies to get quantitative measurement about linguistic goodness of #hashtags.
24. Linguistic Grounding 24 Question 1: Does the tag length distribution of adopted #hashtag demonstrate a different distribution from words? Does it conform to a power law distribution or a lognormal distribution? Do #hashtags of different length receive different goodness judgement (e.g. are extremely short tags better than extremely short words?)
25. Linguistic Grounding 25 Question 2: What are the linguistics processes of creating a #hashtag? What count as a good #hashtag (morphologically, phonotactically, and semantically)? A more qualitative analysis of the #hashtags needs to be done to design a metrics of analysis: e.g. compounding, splinter (of what kind)
26. Linguistic Grounding – behavioral experiements 26 Word vs. Nonword: Subjects will be presented with #hashtags collected from twitter.com, and asked to label them as either word or nonword. Come up with specific criterion for word vs. nonword Morpheme identification: based on the results obtained from the Word vs. Nonword experiment, #hashtags will be presented for subjects to divide them into morphemes and identify meaningful subparts.
27. Linguistic Grounding – behavioral experiements 27 Semantic transparency:word association game: for hashtags like “twitvorce”, subjects will be asked to provide free word associations. For instance, subjects are likely to provide “twitter” and “divorce” for the “twitvorce”.
28. 28 Goodness rating: general: for both #hashtags, that are “nonwords”, subjects will provide subjective goodness ratings, e.g. on a scale from 1 to 7. phonotactic: subjects rate the pronouncability, e.g. for acronyms and initials.For instance, some acronyms are just strings of consonants without vowels, some are strings of vowels, and others are mixture of consonants and vowels. Would more pronouncable #hashtags be perceived as better #hashtags?
29. Linguistic Grounding – statistical parser 29 Phonotactic likelihood: Develop a statistical parser (e.g. finite state machine) for #hashtags and words, and compare the phonotactic probability. Also compare the statistical parser with e.g. Vitevich (2004) model.
30. Social Grounding 30 Based on the realistic data from twitter, diffusion models can be tested. Diffusion models:Linear Threshold ModelCascade Model
31. The Threshold model Threshold Model.It says that people adopt a new behavior because a sufficiently large proportion of their friends have adopted that behavior. E.g. Early adopters have a very low threshold, say 5% or 10%, while late adopters would have a much higher threshold. Every person, however, has their own individual threshold. The key variable here is the initial distribution of thresholds across a social network, which describes in totality the final extent of the behavior. But this model says nothing about how people initially adopt behavior. That is, it says nothing about innovators or the things that are being invented, only about the spread of innovation through a social network. 31
32. The Threshold model 32 In the threshold model every person u has a threshold :and each of their neighbors v is weighted according to: W u,v.If then the person u adopts the behavior. The set of thresholds, weights, and initial adopters determines the extent of the behavior in the social network.
33. The Cascade Model Cascade Modelevery person has a chance of adopting a new behavior whenever one of their neighbors adopts it. The probability that a person adopts the new behavior is the conversion rate for the notification. This probability is both a function of the sender and the recipient, so more influential people are more likely to convince others to adopt a behavior. 33
34. The Cascade Model 34 In the cascade model, for every person u and neighbor v there is a random variable X u,v which describes the likelihood of u adopting the behavior if v has adopted it.
35. Diffusion Model 35 Threshold model: neighborhood densityadopt if enough friends do so. Cascade Model: function of the sender and receiverpeople have a chance of doing something if one of their friends is doing it.
36. Several Challenges at this step 36 Design a metrics for #hashtag classification ( e.g. p. 16): position of #hashtag, functions, word structure. Different #hashtag may have different adoption patterns and diffusion patterns. Quantitative measurement of “success” of a #hashtag: by frequency of mentioning, logevity (within a short or long time frame) Design a way to find competing, equally good #hashtags Representative sample
37. Twitter Network: spot the right network Despite having large networks, a smaller circle is maintained: for users with a high number of followers, they actually only still communicate with a smaller subset of users. Where’s the value? Within the hidden network: find out the true influence model of who people really trust above all other users by looking at actual “@” behavior and follow behavior. 37
November 11th 2009 until February 1st 2010, 14G#Tcot: top conversation on twitter#mm: music monday
D. Zhao and M. B. Rosson. How and why people twitter: the role thatmicro-blogging plays in informal communication at work. In Proceedings of theACM 2009 international conference on Supporting group work. ACM, 2009.C. Wilson, B. Boe, A. Sala, K. P. Puttaswamy, and B. Y. Zhao. User interactionsin social networks and their implications. In Proc. of the 4th ACM Europeanconference on Computer systems. ACM, 2009.J. Weng, E.-P. Lim, J. Jiang, and Q. He. Twitterrank: finding topic-sensitiveinfluential twitterers. In Proc. of the third ACM international conference on Websearch and data mining. ACM, 2010.
Asur, S., and Huberman, B. A. (… )Predicting the Future with Social Mediahttp://www.hpl.hp.com/research/scl/papers/socialmedia/socialmedia.pdfBoyd D. Golder S., and Lotan G. (2010 ) Tweet, Tweet, Retweet: Conversational aspects of retweeting on Twitter. HICSS-43. IEEE: Kauai, HI, January 6.Honeycutt C. and Herring S.C. (2009) Beyond Microblogging: conversation and collaboration via twitter. Proceedings of the forty-second Hawai’I International conference system sciences (HICSS-42) Los Alamitos, CA: IEEE pressFocal Point (game theory) http://en.wikipedia.org/wiki/Focal_point_%28game_theory%29Steels L., and Kaplan F. (1999) Collective learning and semiotic dynamics. In D. Floreano and J.-D. Nicoud and F. Mondada, editors, Advances in Artificial Life: 5th European conference (ECAL 99), Lecture Notes in Artificial Intelligence, 1674, pp. 679-688, Berlin. J. Ke, J.W. Minett, A. Ching-Pong, W.S.-Y,Wang, Selforganization and selection in the emergence of vocabulary, Complexity 7, 41-54 (2002).
The network: simply the network of your followers/followings. Those are the people whose updates you might be reading and who might be receiving your updates. This is the reach of your Twitter stream.The FOAF-network: the network of your followers/follwing’s networks. Those are the people you could potentially reach via retweeting messages. This is the extended reach of your Twitter stream.Asur, S., and Huberman, B. A. (… )Predicting the Future with Social Mediahttp://www.hpl.hp.com/research/scl/papers/socialmedia/socialmedia.pdfFriends and Followers are not the real network@mention and RT indicate a closer network@-conversation: within a one-hour period indicated that about 31% of tweets with @ received a response. (Honeycutt and Herring, 2009).
Physicists in Germany claim to have developed a new computer model that can describe how human languages evolve over time. Dietrich Stauffer and Christian Schulze of Cologne University have taken techniques used by biologists to describe evolution and applied them to the rise and fall of languages. In particular they find that the size distribution of languages - a measure of the relative popularity of different languages - can be described by a nearly "log-normal" curve (arXiv.org/abs/cond-mat/0411162).All languages change over time, with some languages disappearing because they are not spoken by enough people. Stauffer and Schulze describe a particular language by a string of 8 or 16 bits, where each bit can equal 0 or 1, and start their simulations with one person speaking language zero (all bits equal to zero). Two languages are different from each other if they differ by at least one bit. The model works as follows: After a given time, this person produces one offspring who speaks a language that might differ from that spoken by their parent by one bit: the possibility of such a mutation occurring is governed by a probability p. The model also allows for the possibility of a person dying during any iteration: this is governed by a factor called the "carrying capacity" in biology. Lastly, it is also possible that the parent decides to start speaking a different language: this is determined by several factors including the carrying capacity and the fraction of the population who already speak that language. The Cologne physicists found that, for a sample of 10 million people, high mutation rates are needed to ensure that no single language dominates. This finding agrees with data on real languages, as does the prediction that the size distribution of languages is close to a "log-normal" distribution (see figure). "Our model is more realistic than other similar models we know of since it allows for numerous languages, instead of only two," say Stauffer and Schulze. "In these models, only one language survived because it was assumed to be superior to the other. We, on the other hand, have regarded all languages as being equally fit." However, it remains to be seen how the work will be received in the linguistics community. "Linguistics is a relatively new topic for physics and complex systems theory and any tentative way to understand and quantify it is useful and welcome," says Marco Patriarca of the Helsinki University of Technology. "However, while the model Stauffer and Christian Schulze is interesting and worth investigating, it also seems preliminary."