The document discusses using artificial intelligence and machine learning techniques to automatically process and classify information from social media during crisis events. It describes research on classifying tweets and social media posts as related or not related to a crisis, identifying the type of crisis, and determining the type of information in the posts. The research compares traditional machine learning classifiers to deep learning models and finds that semantic features and cross-lingual capabilities improve classification. The goal is to develop tools that can help emergency responders more effectively manage information during disasters.
Classifying Crisis Information Relevancy with Semantics (ESWC 2018)Prashant Khare
Social media platforms have become key portals for sharing and consuming information during crisis situations. However, humanitarian organisations and effected communities often struggle to sieve through the large volumes of data that are typically shared on such platforms during crises to determine which posts are truly relevant to the crisis, and which are not. Previous work on automatically classifying crisis information was mostly focused on using statistical features. However, such approaches tend to be inappropriate when processing data on a type of crisis that the model was not trained on, such as processing information about a train crash, whereas the classifier was trained on floods, earthquakes, and typhoons. In such cases, the model will need to be retrained, which is costly and time-consuming.
In this paper, we explore the impact of semantics in classifying Twitter posts across same, and different, types of crises. We experiment with 26 crisis events, using a hybrid system that combines statistical features with various semantic features extracted from external knowledge bases. We show that adding semantic features has no noticeable benefit over statistical features when classifying same-type crises, whereas it enhances the classifier performance by up to 7.2% when classifying information about a new type of crisis.
Understanding the world with NLP: interactions between society, behaviour and...Diana Maynard
The document discusses analyzing social media data, particularly tweets, for natural language processing tasks. It provides examples of analyzing tweets to understand information sharing during disasters, monitor opinions in real-time, detect topics and analyze political discussions. It also discusses challenges in analyzing tweets like informal language, ambiguity and misleading contexts or hashtags. Precise information extraction and annotation of tweets is needed to accurately identify hate speech, abuse and analyze its targets and changes over time. A multi-step pipeline including collection, preprocessing, information extraction and classification is proposed to understand abuse toward politicians from tweets surrounding UK elections.
Twitris in Action - a review of its many applications Amit Sheth
Twitris is a technology developed at Kno.e.sis that provides real-time, actionable insights from social media data. It analyzes data through approaches like Sentiment-Emotion-Intent, Spatio-Temporal-Thematic, and People-Content-Network. Twitris has been applied in domains like disaster response, elections, public health, and social movements. It has been used to help coordinate responses during crises like hurricanes, floods, and tornadoes.
This is an invited talk I presented at the University of Zurich, speakers' series 2.10.2017. The presentation is based on the following paper: Brandtzaeg, P. B., & Følstad, A. (2017). Trust and distrust in online fact-checking services. Communications of the ACM. 60(9): 65-71
This document summarizes the challenges of computational verification of content shared on social media. It discusses how fake content is often shared quickly on platforms like Twitter, leaving little time for verification. The authors examine using machine learning to verify shared images from events like Hurricane Sandy and the Boston Marathon bombings. They find accuracy is around 80% via cross-validation but drops to 58% when training on one event and testing on the other, highlighting challenges verifying new events. Future work could expand verification to other features and more events.
Social media mining for sensing and responding to real-world trends and eventsYiannis Kompatsiaris
Social media have transformed the Web into an interactive sharing platform where users upload data and media, comment on, and share this content within their social circles. The large-scale availability of user-generated content in social media platforms has opened up new possibilities for studying and understanding real-world phenomena, trends and events. The objective of this talk is to provide an overview of social media mining, which offers a unique opportunity to to discover, collect, and extract relevant information in order to provide useful insights. It will include key challenges and issues, such as fighting misinformation, data collection, analysis and visualization components, applications, results and demos from multiple areas ranging from news to environmental and security ones.
Not-so-obvious Online Data Sources for Demographic ResearchIngmar Weber
Slides from ICWSM'17 workshop on Social Media for Demographic Research (https://sites.google.com/site/smdrworkshop/program). Data sets include Facebook's ad audience estimates, Google Correlate, online genealogy and much more. Contact Ingmar directly to learn more.
Adding value to NLP: a little semantics goes a long wayDiana Maynard
This document discusses how natural language processing (NLP) and semantics can help address challenges in four domains: monitoring violations against journalists, disaster relief, scientometrics, and the "B-word." It provides examples of how NLP tools can extract and categorize information, link entities and events, geotag social media posts, and connect different data sources to provide a richer understanding of knowledge production. Semantic technologies like ontologies are presented as a way to coherently connect topics across document types and data sources.
Classifying Crisis Information Relevancy with Semantics (ESWC 2018)Prashant Khare
Social media platforms have become key portals for sharing and consuming information during crisis situations. However, humanitarian organisations and effected communities often struggle to sieve through the large volumes of data that are typically shared on such platforms during crises to determine which posts are truly relevant to the crisis, and which are not. Previous work on automatically classifying crisis information was mostly focused on using statistical features. However, such approaches tend to be inappropriate when processing data on a type of crisis that the model was not trained on, such as processing information about a train crash, whereas the classifier was trained on floods, earthquakes, and typhoons. In such cases, the model will need to be retrained, which is costly and time-consuming.
In this paper, we explore the impact of semantics in classifying Twitter posts across same, and different, types of crises. We experiment with 26 crisis events, using a hybrid system that combines statistical features with various semantic features extracted from external knowledge bases. We show that adding semantic features has no noticeable benefit over statistical features when classifying same-type crises, whereas it enhances the classifier performance by up to 7.2% when classifying information about a new type of crisis.
Understanding the world with NLP: interactions between society, behaviour and...Diana Maynard
The document discusses analyzing social media data, particularly tweets, for natural language processing tasks. It provides examples of analyzing tweets to understand information sharing during disasters, monitor opinions in real-time, detect topics and analyze political discussions. It also discusses challenges in analyzing tweets like informal language, ambiguity and misleading contexts or hashtags. Precise information extraction and annotation of tweets is needed to accurately identify hate speech, abuse and analyze its targets and changes over time. A multi-step pipeline including collection, preprocessing, information extraction and classification is proposed to understand abuse toward politicians from tweets surrounding UK elections.
Twitris in Action - a review of its many applications Amit Sheth
Twitris is a technology developed at Kno.e.sis that provides real-time, actionable insights from social media data. It analyzes data through approaches like Sentiment-Emotion-Intent, Spatio-Temporal-Thematic, and People-Content-Network. Twitris has been applied in domains like disaster response, elections, public health, and social movements. It has been used to help coordinate responses during crises like hurricanes, floods, and tornadoes.
This is an invited talk I presented at the University of Zurich, speakers' series 2.10.2017. The presentation is based on the following paper: Brandtzaeg, P. B., & Følstad, A. (2017). Trust and distrust in online fact-checking services. Communications of the ACM. 60(9): 65-71
This document summarizes the challenges of computational verification of content shared on social media. It discusses how fake content is often shared quickly on platforms like Twitter, leaving little time for verification. The authors examine using machine learning to verify shared images from events like Hurricane Sandy and the Boston Marathon bombings. They find accuracy is around 80% via cross-validation but drops to 58% when training on one event and testing on the other, highlighting challenges verifying new events. Future work could expand verification to other features and more events.
Social media mining for sensing and responding to real-world trends and eventsYiannis Kompatsiaris
Social media have transformed the Web into an interactive sharing platform where users upload data and media, comment on, and share this content within their social circles. The large-scale availability of user-generated content in social media platforms has opened up new possibilities for studying and understanding real-world phenomena, trends and events. The objective of this talk is to provide an overview of social media mining, which offers a unique opportunity to to discover, collect, and extract relevant information in order to provide useful insights. It will include key challenges and issues, such as fighting misinformation, data collection, analysis and visualization components, applications, results and demos from multiple areas ranging from news to environmental and security ones.
Not-so-obvious Online Data Sources for Demographic ResearchIngmar Weber
Slides from ICWSM'17 workshop on Social Media for Demographic Research (https://sites.google.com/site/smdrworkshop/program). Data sets include Facebook's ad audience estimates, Google Correlate, online genealogy and much more. Contact Ingmar directly to learn more.
Adding value to NLP: a little semantics goes a long wayDiana Maynard
This document discusses how natural language processing (NLP) and semantics can help address challenges in four domains: monitoring violations against journalists, disaster relief, scientometrics, and the "B-word." It provides examples of how NLP tools can extract and categorize information, link entities and events, geotag social media posts, and connect different data sources to provide a richer understanding of knowledge production. Semantic technologies like ontologies are presented as a way to coherently connect topics across document types and data sources.
Weather events identification in social media streams: tools to detect their ...Alfonso Crisci
- The document discusses tools and methods for detecting weather events using social media data, specifically Twitter.
- It describes analyzing Twitter streams related to weather over 4 years to extract metrics and detect impactful weather events in order to increase situational awareness for weather services.
- Key findings include that semantic tuning of Twitter search queries is important to obtain suitable data, and that different search strategies are needed to detect different types of weather events based on their duration and impacts.
On Semantics and Deep Learning for Event Detection in Crisis SituationsCOMRADES project
In this paper, we introduce Dual-CNN, a semantically-enhanced deep learning model to target the problem of event detection in crisis situations from
social media data. A layer of semantics is added to a traditional Convolutional Neural Network (CNN) model to capture the contextual information that is generally scarce in short, ill-formed social media messages. Our results show that
our methods are able to successfully identify the existence of events, and event types (hurricane, floods, etc.) accurately (> 79% F-measure), but the performance of the model significantly drops (61% F-measure) when identifying fine-grained event-related information (affected individuals, damaged infrastructures, etc.).
These results are competitive with more traditional Machine Learning models, such as SVM.
http://oro.open.ac.uk/49639/1/event_detection.pdf
Era of Sociology News Rumors News Detection using Machine Learningijtsrd
In this paper we have perform the political fact checking and fake news detection using various technologies such as Python libraries , Anaconda , and algorithm such as Naïve Bayes, we present an analytical study on the language of news media. To find linguistic features of untrustworthy text, we compare the language of real news with that of satire, hoaxes, and propaganda. We are also presenting a case study based on PolitiFact.com using their factuality judgments on a 6 point scale to prove the feasibility of automatic political fact checking. Experiments show that while media fact checking remains an open research issue, stylistic indications can help determine the veracity of the text. Chandni Jain | S. Vignesh ""Era of Sociology News Rumors News Detection using Machine Learning"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23534.pdf
Paper URL: https://www.ijtsrd.com/computer-science/artificial-intelligence/23534/era-of-sociology-news-rumors-news-detection-using-machine-learning/chandni-jain
Collecting and Coding Twitter Data in DiscoverTextJill Hopke
These are the slides to a workshop I presented on September 23, 2014 to the University of Wisconsin-Madison Digital Humanities Research Network (http://dhresearchnetwork.wordpress.com/). The workshop covered an overview of my research using DiscoverText, steps to collect data in the cloud-based big data analytics software DiscoverText (https://discovertext.com/), and coding data, as well as limitations, challenges and other resources for social media data collection and analysis.
This document discusses using information sharing and social media to build community resilience during emergencies. It notes that communities now expect immediate information and previous responses have created expectations of immediacy. Building resilience involves engaging communities through stakeholder participation, new ideas, informed decisions, empowerment, connectedness and showing how contributions make a difference. Data from surveys on bushfire responses show people rely on information from authorities to decide whether to stay or leave. The document discusses using tools like social media, mobile apps, maps and weather data to improve situational awareness and interoperability between emergency response agencies. It argues for providing information through open standards and being part of online conversations to share safety messages where communities access information.
The RFS uses several technologies and social media platforms in crisis response situations, including line scanning, aircraft cameras, lightning strike data, mobile warnings apps, and multiple Twitter, Facebook, and YouTube accounts. During fires, the RFS provides real-time incident information through RSS feeds and maps on their website. However, technology also poses challenges as loss of communications towers could limit situational awareness, and unsafe wind conditions may ground aircraft and helicopters. The RFS aims to engage in social media conversations to disseminate safety messages while monitoring the dynamic online landscape.
This document provides an overview and definitions of new media and social media. It discusses how the U.S. Fish & Wildlife Service is using new media technologies like blogs, social networking sites, YouTube, Flickr, and wikis to communicate and engage with broad and niche audiences. Examples of how these technologies can be applied for communication, collaboration, education and outreach are also presented.
Hashtags as Publics: Global Frackdown Anti-fracking Movement Twitter PracticesJill Hopke
This document discusses the use of hashtags on Twitter to facilitate transnational environmental movements. It analyzes a dataset of over 9,000 tweets with the #GlobalFrackdown hashtag from October 2013. The tweets came predominantly in English and Spanish and were used to coordinate global actions against fracking, spread awareness of local protests, and build a sense of shared identity and goals across different regions. The study finds that Twitter allows environmental campaigns to combine local and global framing of issues as well as jump between frames in different languages.
Classifying Crises-Information Relevancy with SemanticsCOMRADES project
Prashant Khare, Gregoire Burel, and Harith Alani
Knowledge Media Institute, The Open University, United Kingdom
fprashant.khare,g.burel,h.alanig@open.ac.uk
Towards Explainable Fact Checking (DIKU Business Club presentation)Isabelle Augenstein
Outline:
- Fact checking – what is it and why do we need it?
- False information online
- Content-based automatic fact checking
- Explainability – what is it and why do we need it?
- Making the right predictions for the right reasons
- Model training pipeline
- Explainable fact checking – some first solutions
- Rationale selection
- Generating free-text explanations
- Wrap-up
Semantic Wide and Deep Learning for Detecting Crisis-Information Categories o...Gregoire Burel
When crises hit, many flog to social media to share or consume information related to the event. Social media posts during crises tend to provide valuable reports on affected people, donation offers, help requests, advice provision, etc. Automatically identifying the category of information (e.g., reports on affected individuals, donations and volunteers) contained in these posts is vital for their efficient handling and consumption by effected communities and concerned organisations. In this paper, we introduce Sem-CNN; a wide and deep Convolutional Neural Network (CNN) model designed for identifying the category of information contained in crisis-related social media content. Unlike previous models, which mainly rely on the lexical representations of words in the text, the proposed model integrates an additional layer of semantics that represents the named entities in the text, into a wide and deep CNN network. Results show that the Sem-CNN model consistently outperforms the baselines which consist of statistical and non-semantic deep learning models.
Paper access: http://oro.open.ac.uk/51726/
This document summarizes a student's dissertation on classifying and detecting disaster tweets based on machine learning. The student collected tweets related to disasters like floods and earthquakes to build a dataset. Various machine learning classification algorithms were tested on the dataset, with logistic regression achieving the highest accuracy of 78%. By combining results from all algorithms, an overall accuracy of 79% was achieved in identifying actual disaster tweets. The research aims to help disaster management by providing real-time social media analysis during emergencies.
Making Your ClaimBe specificMaking your claim specific.docxinfantsuk
Making Your Claim
Be specific
Making your claim specific
Vague claims lead to vague arguments.
The more specific your claim, the more it helps you plan your argument.
Compare these claims
1) There are challenges and risks associated with using social media in disaster/crises situations but also some benefits.
2) The benefits of using social media in disaster/crises situations far outweigh the challenges and risks because social media’s capabilities such as mapping services, messages, group collaboration platforms and more serve as powerful tools for emergency response operations.
Making specific claims
The first claim is so vague we don’t have any idea what the writer will develop in the paper.
The second claim has concepts that not only help us understand the claim more clearly, but also give the writer a clear set of concepts to develop in the paper.
How to write a ‘working claim’
The second claim is a bit long but the best way to develop a claim is to write a ‘draft’ claim and look at the themes you want to develop from that claim. This will lead you to your specific final claim.
How to make a claim
Introduce your claim with a qualifying clause beginning with ‘although’ or ‘even though’
Example 1:
Although/Even though there are challenges and risks associated with social media in disaster/crises situations …….
State your claim
(I claim that) the benefits of using social media in these situations far outweigh the risks…..
Follow this with a ‘because’ clause
because social media capabilities such as mapping services, messages, group collaboration platforms and more serve as powerful tools for emergency response operations.
Concepts to develop from this claim
1.Explain the challenges and risks associated with using social media as identified by the authors of the three articles.
2. Explain the benefits of using social media in disaster/crises situations as identified by the authors of the three articles. Itemize or pay particular attention to the capabilities you list e.g. mapping etc.
3. Support your claim by explaining how the authors of the three studies found that these social media capabilities served as powerful tools for emergency response operations.
Claim #2
Although/Even though public officials/disaster management teams are concerned about the chaotic, subjective and totally unverifiable nature of information that circulates through social media applications,
(I claim that) social media have the potential to facilitate public participation and communication between governmental institutions and citizens
because individuals who are using social media have already developed a great bond of trust with other members of their online social communities.
Concepts to develop through this claim
1. Why/how is the information that circulates through social media applications chaotic, subjective and totally unverifiable? How do the findings of the 3 authors of the studies support this claim?
2. How/why a ...
Analytic Journalism: Investing in an Intellectual Portfolio to Secure Journal...J T "Tom" Johnson
The document discusses the changing information environment and the need for journalism to adapt through analytic journalism. It defines analytic journalism as using tools from other fields like statistics, visualization, and modeling to research, analyze, and report on stories. The document provides examples of how tools from fields like epidemiology, urban planning, and computational linguistics have been applied to journalism, and suggests journalism needs to invest in developing these analytic skills to survive in the current data-rich environment.
This document provides guidance on writing effective paragraphs, including six common paragraph patterns and techniques for developing strong topic sentences and verb-based writing. It discusses chronological, classification/listing, evidence/illustration, compare/contrast, question/answer, and cause/effect paragraph structures. Key connectors for each pattern are provided. The importance of clear topic sentences and verb-based writing for engagement and readability is emphasized. Strategies like analyzing topic sentences, practicing rewrites, and identifying nominalizations are presented to help improve writing skills.
Quantitative and Digital Skills of International Journalism and Communication...J T "Tom" Johnson
This document summarizes a survey of journalism and communications educators around the world. The survey found that educators lag behind changes in data and technology, making limited use of advanced analytic tools and digital communications. Journalism education focuses more on writing than data analysis. While definitions of journalism are evolving, curricula do not fully reflect changes in how data is processed and used. This disconnect between journalism practice and education could lead to superficial reporting and less relevance for democracy over time.
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.
Social media is a valuable source of information for different domains, since users share their opinion and knowledge in (near) real-time. Moreover, users usually use different words to refer to a particular event (e.g., a rain event). These words may be later employed to filter social media messages regarding new occurrences of the event and, thus, to reduce the number of unrelated messages. These words, however, may have different meanings and, thus, may not reduce the number of messages. In this work, we conduct a case study to measure which rain- or flood-related keywords are less relevant to reduce the number of unrelated messages. The results show that the keywords change over space, due to local language/culture, and time, specially in different time scales.
Harnessing Digital And Social Media To Become A Learning OrganizationDr. William J. Ward
The document discusses how organizations can harness digital and social media to become learning organizations. It recommends five things to do today: 1) Create conditions for learning, innovation and engagement by making social media everyone's job; 2) Encourage participation by finding experts and sharing content; 3) Set objectives, measure outputs, and incentivize/reward participation; 4) Use social bookmarking, curation and content management; 5) Strengthen teams through collaboration and group discussions. Harnessing social media requires organizations to embrace radical openness, take problems to where people already are, and energize employees through participation.
Keynote delivered at ACM Hypertext conference on 6th of September 2023.
Abstract: You’re probably getting a bit worn out from all these talks about misinformation and Twitter-based experiments. The fact that Twitter is now called Platform X is probably not enough of a change to keep you awake during my talk! But I think, or hope, to bring up a few things in this talk that you might not have come across or thought about much before. I believe that having fact-checks that call out false or misleading claims is very important in our fight against misinformation. But we’re still not quite sure if and how they impact the spread of wrong information and how they could help set things right online. So, in this talk, I’ll dive into how we’re all prone to falling for misinformation and make a case for needing data and tools to help us see how both ourselves and others engage with false or unreliable information over long periods of time. I’ll also share what we’ve learnt from our research about how these fact-checks affect how wrong info spreads, and I’ll give you the scoop on what happened when we tried using automatic replies to correct misinforming posts on Twitter, oops, I mean platform X. If all of this still feels like old news to you, well, there’s always that email inbox to keep you awake during my keynote.
Keynote at the 2nd International Workshop on Knowledge Graphs for Online Discourse Analysis (BeyondFacts’22) – April 26, 2022
Talk abstract: Misinformation has always been part of humankind’s information ecosystem. The development of tools and methods for automatically detecting the reliability of information has received a great deal of attention in recent years, such as calculating the authenticity of images, calculating the likelihood of claims, and assessing the credibility of sources. Unfortunately, there is little evidence that the presence of these advanced technologies or the constant effort of fact-checkers worldwide can help stop the spread of misinformation. I will try to convince you that you also hold various false beliefs, and argue for the need for technologies and processes to assess the information shared by ourselves or by others, over a longer period of time, in order to improve our knowledge of our information credibility and vulnerability, as well as those of the people we listen to. Also, I will describe the benefits, challenges, and risks of automated information corrective actions, both for the target recipients and their wider audience.
More Related Content
Similar to Crisis Information Processing - with the power of A.I.
Weather events identification in social media streams: tools to detect their ...Alfonso Crisci
- The document discusses tools and methods for detecting weather events using social media data, specifically Twitter.
- It describes analyzing Twitter streams related to weather over 4 years to extract metrics and detect impactful weather events in order to increase situational awareness for weather services.
- Key findings include that semantic tuning of Twitter search queries is important to obtain suitable data, and that different search strategies are needed to detect different types of weather events based on their duration and impacts.
On Semantics and Deep Learning for Event Detection in Crisis SituationsCOMRADES project
In this paper, we introduce Dual-CNN, a semantically-enhanced deep learning model to target the problem of event detection in crisis situations from
social media data. A layer of semantics is added to a traditional Convolutional Neural Network (CNN) model to capture the contextual information that is generally scarce in short, ill-formed social media messages. Our results show that
our methods are able to successfully identify the existence of events, and event types (hurricane, floods, etc.) accurately (> 79% F-measure), but the performance of the model significantly drops (61% F-measure) when identifying fine-grained event-related information (affected individuals, damaged infrastructures, etc.).
These results are competitive with more traditional Machine Learning models, such as SVM.
http://oro.open.ac.uk/49639/1/event_detection.pdf
Era of Sociology News Rumors News Detection using Machine Learningijtsrd
In this paper we have perform the political fact checking and fake news detection using various technologies such as Python libraries , Anaconda , and algorithm such as Naïve Bayes, we present an analytical study on the language of news media. To find linguistic features of untrustworthy text, we compare the language of real news with that of satire, hoaxes, and propaganda. We are also presenting a case study based on PolitiFact.com using their factuality judgments on a 6 point scale to prove the feasibility of automatic political fact checking. Experiments show that while media fact checking remains an open research issue, stylistic indications can help determine the veracity of the text. Chandni Jain | S. Vignesh ""Era of Sociology News Rumors News Detection using Machine Learning"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23534.pdf
Paper URL: https://www.ijtsrd.com/computer-science/artificial-intelligence/23534/era-of-sociology-news-rumors-news-detection-using-machine-learning/chandni-jain
Collecting and Coding Twitter Data in DiscoverTextJill Hopke
These are the slides to a workshop I presented on September 23, 2014 to the University of Wisconsin-Madison Digital Humanities Research Network (http://dhresearchnetwork.wordpress.com/). The workshop covered an overview of my research using DiscoverText, steps to collect data in the cloud-based big data analytics software DiscoverText (https://discovertext.com/), and coding data, as well as limitations, challenges and other resources for social media data collection and analysis.
This document discusses using information sharing and social media to build community resilience during emergencies. It notes that communities now expect immediate information and previous responses have created expectations of immediacy. Building resilience involves engaging communities through stakeholder participation, new ideas, informed decisions, empowerment, connectedness and showing how contributions make a difference. Data from surveys on bushfire responses show people rely on information from authorities to decide whether to stay or leave. The document discusses using tools like social media, mobile apps, maps and weather data to improve situational awareness and interoperability between emergency response agencies. It argues for providing information through open standards and being part of online conversations to share safety messages where communities access information.
The RFS uses several technologies and social media platforms in crisis response situations, including line scanning, aircraft cameras, lightning strike data, mobile warnings apps, and multiple Twitter, Facebook, and YouTube accounts. During fires, the RFS provides real-time incident information through RSS feeds and maps on their website. However, technology also poses challenges as loss of communications towers could limit situational awareness, and unsafe wind conditions may ground aircraft and helicopters. The RFS aims to engage in social media conversations to disseminate safety messages while monitoring the dynamic online landscape.
This document provides an overview and definitions of new media and social media. It discusses how the U.S. Fish & Wildlife Service is using new media technologies like blogs, social networking sites, YouTube, Flickr, and wikis to communicate and engage with broad and niche audiences. Examples of how these technologies can be applied for communication, collaboration, education and outreach are also presented.
Hashtags as Publics: Global Frackdown Anti-fracking Movement Twitter PracticesJill Hopke
This document discusses the use of hashtags on Twitter to facilitate transnational environmental movements. It analyzes a dataset of over 9,000 tweets with the #GlobalFrackdown hashtag from October 2013. The tweets came predominantly in English and Spanish and were used to coordinate global actions against fracking, spread awareness of local protests, and build a sense of shared identity and goals across different regions. The study finds that Twitter allows environmental campaigns to combine local and global framing of issues as well as jump between frames in different languages.
Classifying Crises-Information Relevancy with SemanticsCOMRADES project
Prashant Khare, Gregoire Burel, and Harith Alani
Knowledge Media Institute, The Open University, United Kingdom
fprashant.khare,g.burel,h.alanig@open.ac.uk
Towards Explainable Fact Checking (DIKU Business Club presentation)Isabelle Augenstein
Outline:
- Fact checking – what is it and why do we need it?
- False information online
- Content-based automatic fact checking
- Explainability – what is it and why do we need it?
- Making the right predictions for the right reasons
- Model training pipeline
- Explainable fact checking – some first solutions
- Rationale selection
- Generating free-text explanations
- Wrap-up
Semantic Wide and Deep Learning for Detecting Crisis-Information Categories o...Gregoire Burel
When crises hit, many flog to social media to share or consume information related to the event. Social media posts during crises tend to provide valuable reports on affected people, donation offers, help requests, advice provision, etc. Automatically identifying the category of information (e.g., reports on affected individuals, donations and volunteers) contained in these posts is vital for their efficient handling and consumption by effected communities and concerned organisations. In this paper, we introduce Sem-CNN; a wide and deep Convolutional Neural Network (CNN) model designed for identifying the category of information contained in crisis-related social media content. Unlike previous models, which mainly rely on the lexical representations of words in the text, the proposed model integrates an additional layer of semantics that represents the named entities in the text, into a wide and deep CNN network. Results show that the Sem-CNN model consistently outperforms the baselines which consist of statistical and non-semantic deep learning models.
Paper access: http://oro.open.ac.uk/51726/
This document summarizes a student's dissertation on classifying and detecting disaster tweets based on machine learning. The student collected tweets related to disasters like floods and earthquakes to build a dataset. Various machine learning classification algorithms were tested on the dataset, with logistic regression achieving the highest accuracy of 78%. By combining results from all algorithms, an overall accuracy of 79% was achieved in identifying actual disaster tweets. The research aims to help disaster management by providing real-time social media analysis during emergencies.
Making Your ClaimBe specificMaking your claim specific.docxinfantsuk
Making Your Claim
Be specific
Making your claim specific
Vague claims lead to vague arguments.
The more specific your claim, the more it helps you plan your argument.
Compare these claims
1) There are challenges and risks associated with using social media in disaster/crises situations but also some benefits.
2) The benefits of using social media in disaster/crises situations far outweigh the challenges and risks because social media’s capabilities such as mapping services, messages, group collaboration platforms and more serve as powerful tools for emergency response operations.
Making specific claims
The first claim is so vague we don’t have any idea what the writer will develop in the paper.
The second claim has concepts that not only help us understand the claim more clearly, but also give the writer a clear set of concepts to develop in the paper.
How to write a ‘working claim’
The second claim is a bit long but the best way to develop a claim is to write a ‘draft’ claim and look at the themes you want to develop from that claim. This will lead you to your specific final claim.
How to make a claim
Introduce your claim with a qualifying clause beginning with ‘although’ or ‘even though’
Example 1:
Although/Even though there are challenges and risks associated with social media in disaster/crises situations …….
State your claim
(I claim that) the benefits of using social media in these situations far outweigh the risks…..
Follow this with a ‘because’ clause
because social media capabilities such as mapping services, messages, group collaboration platforms and more serve as powerful tools for emergency response operations.
Concepts to develop from this claim
1.Explain the challenges and risks associated with using social media as identified by the authors of the three articles.
2. Explain the benefits of using social media in disaster/crises situations as identified by the authors of the three articles. Itemize or pay particular attention to the capabilities you list e.g. mapping etc.
3. Support your claim by explaining how the authors of the three studies found that these social media capabilities served as powerful tools for emergency response operations.
Claim #2
Although/Even though public officials/disaster management teams are concerned about the chaotic, subjective and totally unverifiable nature of information that circulates through social media applications,
(I claim that) social media have the potential to facilitate public participation and communication between governmental institutions and citizens
because individuals who are using social media have already developed a great bond of trust with other members of their online social communities.
Concepts to develop through this claim
1. Why/how is the information that circulates through social media applications chaotic, subjective and totally unverifiable? How do the findings of the 3 authors of the studies support this claim?
2. How/why a ...
Analytic Journalism: Investing in an Intellectual Portfolio to Secure Journal...J T "Tom" Johnson
The document discusses the changing information environment and the need for journalism to adapt through analytic journalism. It defines analytic journalism as using tools from other fields like statistics, visualization, and modeling to research, analyze, and report on stories. The document provides examples of how tools from fields like epidemiology, urban planning, and computational linguistics have been applied to journalism, and suggests journalism needs to invest in developing these analytic skills to survive in the current data-rich environment.
This document provides guidance on writing effective paragraphs, including six common paragraph patterns and techniques for developing strong topic sentences and verb-based writing. It discusses chronological, classification/listing, evidence/illustration, compare/contrast, question/answer, and cause/effect paragraph structures. Key connectors for each pattern are provided. The importance of clear topic sentences and verb-based writing for engagement and readability is emphasized. Strategies like analyzing topic sentences, practicing rewrites, and identifying nominalizations are presented to help improve writing skills.
Quantitative and Digital Skills of International Journalism and Communication...J T "Tom" Johnson
This document summarizes a survey of journalism and communications educators around the world. The survey found that educators lag behind changes in data and technology, making limited use of advanced analytic tools and digital communications. Journalism education focuses more on writing than data analysis. While definitions of journalism are evolving, curricula do not fully reflect changes in how data is processed and used. This disconnect between journalism practice and education could lead to superficial reporting and less relevance for democracy over time.
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.
Social media is a valuable source of information for different domains, since users share their opinion and knowledge in (near) real-time. Moreover, users usually use different words to refer to a particular event (e.g., a rain event). These words may be later employed to filter social media messages regarding new occurrences of the event and, thus, to reduce the number of unrelated messages. These words, however, may have different meanings and, thus, may not reduce the number of messages. In this work, we conduct a case study to measure which rain- or flood-related keywords are less relevant to reduce the number of unrelated messages. The results show that the keywords change over space, due to local language/culture, and time, specially in different time scales.
Harnessing Digital And Social Media To Become A Learning OrganizationDr. William J. Ward
The document discusses how organizations can harness digital and social media to become learning organizations. It recommends five things to do today: 1) Create conditions for learning, innovation and engagement by making social media everyone's job; 2) Encourage participation by finding experts and sharing content; 3) Set objectives, measure outputs, and incentivize/reward participation; 4) Use social bookmarking, curation and content management; 5) Strengthen teams through collaboration and group discussions. Harnessing social media requires organizations to embrace radical openness, take problems to where people already are, and energize employees through participation.
Similar to Crisis Information Processing - with the power of A.I. (20)
Keynote delivered at ACM Hypertext conference on 6th of September 2023.
Abstract: You’re probably getting a bit worn out from all these talks about misinformation and Twitter-based experiments. The fact that Twitter is now called Platform X is probably not enough of a change to keep you awake during my talk! But I think, or hope, to bring up a few things in this talk that you might not have come across or thought about much before. I believe that having fact-checks that call out false or misleading claims is very important in our fight against misinformation. But we’re still not quite sure if and how they impact the spread of wrong information and how they could help set things right online. So, in this talk, I’ll dive into how we’re all prone to falling for misinformation and make a case for needing data and tools to help us see how both ourselves and others engage with false or unreliable information over long periods of time. I’ll also share what we’ve learnt from our research about how these fact-checks affect how wrong info spreads, and I’ll give you the scoop on what happened when we tried using automatic replies to correct misinforming posts on Twitter, oops, I mean platform X. If all of this still feels like old news to you, well, there’s always that email inbox to keep you awake during my keynote.
Keynote at the 2nd International Workshop on Knowledge Graphs for Online Discourse Analysis (BeyondFacts’22) – April 26, 2022
Talk abstract: Misinformation has always been part of humankind’s information ecosystem. The development of tools and methods for automatically detecting the reliability of information has received a great deal of attention in recent years, such as calculating the authenticity of images, calculating the likelihood of claims, and assessing the credibility of sources. Unfortunately, there is little evidence that the presence of these advanced technologies or the constant effort of fact-checkers worldwide can help stop the spread of misinformation. I will try to convince you that you also hold various false beliefs, and argue for the need for technologies and processes to assess the information shared by ourselves or by others, over a longer period of time, in order to improve our knowledge of our information credibility and vulnerability, as well as those of the people we listen to. Also, I will describe the benefits, challenges, and risks of automated information corrective actions, both for the target recipients and their wider audience.
Talk delivered at the Paris Peace Forum, Nov 12-13, where I presented the H2020 Co-Inform project that aims at researching and developing socio-technical tools to tackle misinformation.
SASIG Workshop on “Improving the digital landscape for our children”The Open University
Reflections on the Online Harms White Paper published in April 2019. https://www.gov.uk/government/consultations/online-harms-white-paper
Presented these slides as part of a panel. Agenda of the workshop: https://gallery.mailchimp.com/6a29a22efa92c19681485a0ee/files/f3d318a3-978e-4977-be85-971ecb97ca13/Child_Safety_Online_Agenda_v33.pdf
The COMRADES project aims to develop an intelligent platform to empower communities to respond to and recover from crisis situations using social media. Led by Harith Alani at the Open University, the consortium includes Ushahidi, Delft University of Technology, and University of Sheffield. The project will extract requirements, identify emergency events on social media, assess information validity, and deploy the COMRADES platform to support communities during live crises. It will run in two periods over multiple crisis types and aims to produce 30 peer-reviewed publications and 40 scientific outputs to empower local communities through information sharing.
Co-Inform (Co-Creating Misinformation Resilient Societies)The Open University
This document summarizes a presentation on co-creating resilience to misinformation. It discusses the challenges fact checkers face in keeping up with misinformation spread on social media and the unclear impact of fact checks. The goals of the Co-Inform project are outlined, including understanding misinformation flow, creating detection tools, and making recommendations. The presentation notes several existing misinformation detection tools and plugins. It promotes developing self-awareness through tools assessing an individual's interactions with and spread of misinformation within their own network.
The document discusses the COMRADES project, which aims to develop an intelligent platform to help communities reconnect, respond to, and recover from crisis situations using social media. The project is funded by the European Union's Horizon 2020 program and involves several European universities and organizations. It plans to extract requirements for resilience platforms, identify emergency events on social media, assess crisis information validity, and deploy the platform for communities during real disasters.
This document discusses detecting online radicalization through social media. It presents several approaches for identifying signals of radicalization using machine learning techniques, including lexicon-based approaches that analyze language use and knowledge graphs that extract semantic relationships between entities and concepts. The goal is to automatically classify social media accounts as non-violent radical, non-radical, or violent radical in order to more effectively and less biased detect the radicalization process.
The document discusses online child grooming and radicalization. It begins by defining child grooming as premeditated behavior intended to secure the trust of a minor for future sexual conduct. It then provides an example conversation between a predator and minor to demonstrate grooming techniques. Next, it discusses approaches for automatically identifying the stages of grooming (approach, trust development, isolation, physical approach) using machine learning classifiers. It achieves up to 88% accuracy. Finally, it discusses detecting online radicalization, including approaches using semantic analysis, knowledge graphs, and classifying social media accounts as radical or non-radical using machine learning.
The document discusses detecting online grooming and radicalization through social media analytics. It provides background on child grooming and statistics on its prevalence from sources like the NSPCC and CEOP. Examples of online grooming conversations are presented, showing how predators use approaches like compliments, requests for photos, developing trust and isolation to groom their victims. Methods for automatically identifying the stages of grooming through classifiers trained on annotated datasets are described, achieving over 80% precision and recall. The document also covers online radicalization, presenting models of radicalization and research on detecting signals of radicalization on social media through machine learning and lexicon-based approaches.
The document discusses detecting online grooming behavior on social media. It provides definitions of child grooming and online grooming as premeditated behaviors to gain a minor's trust to engage in future sexual conduct. Statistics are presented on the number of UK children who experience sexual abuse. Theories of online grooming behaviors are described, including establishing rapport, isolating the child, and introducing sexual topics. An example conversation shows the gradual process of an online predator engaging in grooming language and attempts to escalate contact over several messages.
Mining and Comparing Engagement Dynamics Across Multiple Social Media Platfor...The Open University
Understanding what attracts users to engage with social media content is important in domains such as market analytics, advertising, and community management.
To date, many pieces of work have examined engagement dynamics in isolated platforms with little consideration or assessment of how these dynamics might vary between disparate social media systems. Additionally, such explorations have often used different features and notions of engagement, thus rendering the cross-platform comparison of engagement dynamics limited. In this paper we define a common framework of engagement analysis and examine and compare engagement dynamics across five social media platforms: Facebook, Twitter, Boards.ie, Stack Overflow and the SAP Community Network. We define a variety of common features (social and content) to capture the dynamics that correlate with engagement in multiple social media platforms, and present an evaluation pipeline intended to enable cross-platform comparison. Our comparison results demonstrate the varying factors at play in different platforms, while also exposing several similarities.
Short presentation at Dagstuhl seminar on Physical-Cyber-Social Computing, September 29 to October 4, 2013.
http://www.dagstuhl.de/en/program/calendar/semhp/?semnr=13402
Gave this talk at SSSW'13; The 10th Summer School on Ontology Engineering and the Semantic Web
7 - 13 July, 2013. Cercedilla, Spain. http://sssw.org/2013/
Harith Alani is a researcher at the Knowledge Media Institute (KMi) at the Open University. KMi focuses on research and development related to the future internet, knowledge management, multimedia systems, and more. Alani's work involves analyzing behavior and health of online communities through semantic profiling, social network analysis, and machine learning models. He has published over 100 papers on topics such as predicting valuable community members, detecting influential content, and understanding community evolution over time.
This document discusses monitoring and analyzing online communities. It begins by outlining tools for monitoring social media mentions, sentiment, discussion activity and more. It then discusses measuring social media usage in companies and tools for analyzing community features like influence, opinions and geolocation. The document explores merging offline and online social networks using sensors and integrating physical presence data with online profiles and semantic analysis. It provides examples of tracking face-to-face contact networks and analyzing characteristics of offline social networks.
This document discusses research on analyzing social networks using radio-frequency identification (RFID) devices to track face-to-face interactions between individuals in real-world settings like conferences. It describes deployments of RFID badge systems to log social contacts at various events and outlines approaches for merging offline contact networks with online social networking data to generate semantic profiles of users and communities. Key findings from analyzing face-to-face contact data include characteristics of interaction patterns, correlations between scientific experience and networking behavior, and a lack of strong correlation between offline social activity and size of online social networks.
Telegram is a messaging platform that ushers in a new era of communication. Available for Android, Windows, Mac, and Linux, Telegram offers simplicity, privacy, synchronization across devices, speed, and powerful features. It allows users to create their own stickers with a user-friendly editor. With robust encryption, Telegram ensures message security and even offers self-destructing messages. The platform is open, with an API and source code accessible to everyone, making it a secure and social environment where groups can accommodate up to 200,000 members. Customize your messenger experience with Telegram's expressive features.
EASY TUTORIAL OF HOW TO USE G-TEAMS BY: FEBLESS HERNANEFebless Hernane
Using Google Teams (G-Teams) is simple. Start by opening the Google Teams app on your phone or visiting the G-Teams website on your computer. Sign in with your Google account. To join a meeting, click on the link shared by the organizer or enter the meeting code in the "Join a Meeting" section. To start a meeting, click on "New Meeting" and share the link with others. You can use the chat feature to send messages and the video button to turn your camera on or off. G-Teams makes it easy to connect and collaborate with others!
EASY TUTORIAL OF HOW TO USE REMINI BY: FEBLESS HERNANEFebless Hernane
Using Remini is easy and quick for enhancing your photos. Start by downloading the Remini app on your phone. Open the app and sign in or create an account. To improve a photo, tap the "Enhance" button and select the photo you want to edit from your gallery. Remini will automatically enhance the photo, making it clearer and sharper. You can compare the before and after versions by swiping the screen. Once you're happy with the result, tap "Save" to store the enhanced photo in your gallery. Remini makes your photos look amazing with just a few taps!
This tutorial presentation offers a beginner-friendly guide to using THREADS, Instagram's messaging app. It covers the basics of account setup, privacy settings, and explores the core features such as close friends lists, photo and video sharing, creative tools, and status updates. With practical tips and instructions, this tutorial will empower you to use THREADS effectively and stay connected with your close friends on Instagram in a private and engaging way.
Project Serenity is an innovative initiative aimed at transforming urban environments into sustainable, self-sufficient communities. By integrating green architecture, renewable energy, smart technology, sustainable transportation, and urban farming, Project Serenity seeks to minimize the ecological footprint of cities while enhancing residents' quality of life. Key components include energy-efficient buildings, IoT-enabled resource management, electric and autonomous transportation options, green spaces, and robust waste management systems. Emphasizing community engagement and social equity, Project Serenity aspires to serve as a global model for creating eco-friendly, livable urban spaces that harmonize modern conveniences with environmental stewardship.
Your LinkedIn Success Starts Here.......SocioCosmos
In order to make a lasting impression on your sector, SocioCosmos provides customized solutions to improve your LinkedIn profile.
https://www.sociocosmos.com/product-category/linkedin/
The Evolution of SEO: Insights from a Leading Digital Marketing AgencyDigital Marketing Lab
Explore the latest trends in Search Engine Optimization (SEO) and discover how modern practices are transforming business visibility. This document delves into the shift from keyword optimization to user intent, highlighting key trends such as voice search optimization, artificial intelligence, mobile-first indexing, and the importance of E-A-T principles. Enhance your online presence with expert insights from Digital Marketing Lab, your partner in maximizing SEO performance.
This tutorial presentation provides a step-by-step guide on how to use Facebook, the popular social media platform. In simple and easy-to-understand language, this presentation explains how to create a Facebook account, connect with friends and family, post updates, share photos and videos, join groups, and manage privacy settings. Whether you're new to Facebook or just need a refresher, this presentation will help you navigate the features and make the most of your Facebook experience.
Surat Digital Marketing School is created to offer a complete course that is specifically designed as per the current industry trends. Years of experience has helped us identify and understand the graduate-employee skills gap in the industry. At our school, we keep up with the pace of the industry and impart a holistic education that encompasses all the latest concepts of the Digital world so that our graduates can effortlessly integrate into the assigned roles.
This is the place where you become a Digital Marketing Expert.
Lifecycle of a GME Trader: From Newbie to Diamond Handsmediavestfzllc
Your phone buzzes with a Reddit notification. It's the WallStreetBets forum, a cacophony of memes, rocketship emojis, and fervent discussions about Gamestop (GME) stock. A spark ignites within you - a mix of internet bravado, a rebellious urge to topple the hedge funds (remember Mr. Mayo?), and maybe that one late-night YouTube rabbit hole about tendies. You decide to YOLO (you only live once, right?).
Ramen noodles become your new best friend. Every spare penny gets tossed into the GME piggy bank. You're practically living on fumes, but the dream of a moonshot keeps you going. Your phone becomes an extension of your hand, perpetually glued to the GME ticker. It's a roller-coaster ride - every dip a stomach punch, every rise a shot of adrenaline.
Then, it happens. Roaring Kitty, the forum's resident legend, fires off a cryptic tweet. The apes, as the GME investors call themselves, erupt in a frenzy. Could this be it? Is the rocket finally fueled for another epic launch? You grip your phone tighter, heart pounding in your chest. It's a wild ride, but you're in it for the long haul.
11. DISASTER
RESPONSE
THROUGH SOCIAL
MEDIA
“The models that are emerging indicate
that affected people are becoming
extremely adept at using social media
platforms in particular to engage in
networked systems of response. This
means they are able to post about specific
needs and solicit individual responses to
those needs, and that people offering
specific help can also do so”
15. ”Immediate damage estimates based on FEMA
models can miss areas of heavy impact. Augmenting
initial models with real-time analysis of social media
and crowdsourced information can help identify
overlooked areas. Twitter-sourced estimates were
virtually available as people tweeted distress signals,
of these parcel-level damage estimates, 46 percent
were not captured by FEMA estimates.”
FEMA MISSES
HURRICANE
DAMAGE
REPORTED ON
TWITTER
18. WORKFLOW OF USHAHIDI & SIMILAR PLATFORMS
citizen reporters digital responders
Manual
Annotations
administrators
Manual
Verification
Manual
Publishing
analysts/public/
research teams
19. SOCIAL MEDIA INFOSMOG DURING
DISASTERS
In the US, 1.1 million tweets were sent in the first day of Hurricane Sandy, and
over 20 million in total
~800K photos with #Sandy hashtag on Instagram
More than 23 million tweets were posted about the haze in Singapore
In Nepal, more than half a million posts were shared about the devastating
earthquake in 2015
>2.3M tweets were sent with the words “Haiti” or “Red Cross” in 2010
~177 million tweets sent about the Japan 2011 earthquake disaster
21. REQUIREMENTS & CHALLENGES
VOLUME
VALUE
VARIETY
VALIDITY
Too much content to handle manually
More content is coming in all the time
Rumours and hoaxes
spread wild during
disasters
Content is often repetitive and
uninformative
Much of the content is irrelevant
VELOCITY
22. Filtering out irrelevant information helps to
tackle information overload
How do we identify relevant and irrelevant
information across diverse crises
situations?
Can we learn from one type of crisis
situation, and apply it to another?
Can we train our models on one language
and apply it to another?
RELEVANCY OF
SOCIAL MEDIA
POSTS
28. SVM (20 iterations 5- fold cross validation)
Features P R F
0.81 0.81 0.81Statistical Features
PRECISION RECALL F-MEASURE
TRAIN & TEST ON SAME CRISES EVENTS
What if we add some domain knowledge?
31. SVM (20 iterations 5- fold cross validation)
Features P R F
0.81 0.81 0.81 -
0.82 0.82 0.82 1.39
0.81 0.81 0.81 0.33
0.82 0.82 0.82 0.6
Semantic Features
Statistical Features
PRECISION RECALL F-MEASURE
∆F /F
(%)
Semantic Features
Semantic Features
TRAIN & TEST ON SAME CRISES EVENTS
34. CLASSIFYING FAMILIAR EVENTS
Train model on all data,
then test on a new crisis
event of a type the was in
the training set
Eg., train model on data
that include flood events,
then test on a new flood
crisis event
Adding semantic features
offer modest improvements
over statistical features
alone
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
TyphoonYolanda
TyphoonPablo
AlbertaFlood
QueenslandFlood
ColoradoFloods
PhilippinesFlood
SardiniaFlood
GuatemalaEarthquake
ItalyEarthquake
BoholEarthquake
CostaRicaEarthquake
average
F-Measure
Statistical Features Semantic Features
Flood/Typhoon Earthquake
∆ 1.7%
35. CLASSIFYING UNFAMILIAR EVENTS
Train model on certain type
of events, and test it on
other types
E.g., train model on data
that include flood and
earthquake events, then
test on a train crash
incident
Adding semantic features
offer a good improvement
over statistical features
alone
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
LAAirportShoot
LacMeganticTrainCrash
BostonBombing
SpainTrainCrash
TyphoonYolanda
TyphoonPablo
AlbertaFlood
QueenslandFlood
ColoradoFloods
PhilippinesFlood
SardiniaFlood
GuatemalaEarthquake
ItalyEarthquake
BoholEarthquake
CostaRicaEarthquake
average
F-Measure
Statistical Features Semantic Features
Terror/Bomb/Train Flood/Typhoon Earthquake
Khare, P.; Burel, G. and Alani, H. Classifying Crises-Information Relevancy with Semantics. Extended Semantic Web Conference (ESWC), Heraklion, Crete, 2018.
∆ 7.2%
37. CLASSIFYING MULTILINGUAL CRISES DATA
Monolingual Classification
with Monolingual Models
Cross-lingual Classification
with Monolingual Models
Train the model on one language and
test it on data in the same language.
For example, train and test on data
written in English. This is the default
approach, and can be used as a
baseline.
Run the classifiers on crisis data in
languages that were not observed in
the training data. For example, we
test the classifier on Italian when the
classifier was trained on English or
Spanish.
Cross-lingual Classification
with Machine Translation
Train the classification model on data
in a certain language (e.g. Spanish),
and use it to classify data that has
been automatically translated from
other languages (e.g., Italian and
English) into the language of the
training data.
38. Khare, P., Burel, G., Maynard, D., and Alani, H. Cross-Lingual Classification of Crisis Data. Int. Semantic Web Conference, Monterey, CA, USA, 2018
Around 9% improvement in
detecting crisis-data
relevancy when training on
one language and applying it
on another
0.429
0.688
0.521
0.64
0.578
0.489
0.5570.572
0.659
0.538
0.631 0.65
0.543
0.599
English [Italian] English [Spanish] Italian [English] Italian [Spanish] Spanish [English] Spanish [Italian] average
Cross-lingual Classification
with Monolingual Models
Machine translation offers
good classification
improvements without any
semantics
0.546
0.669
0.572
0.609
0.675
0.593
0.633
0.581
0.664
0.551
0.582
0.683
0.571
0.605
English [Italian-
>English]
English [Spanish-
>English]
Italiant [English-
>Italian]
Italiant [Spanish-
>Italian]
Spanish [English-
>Spanish]
Spanish [Italian-
>Spanish]
average
Cross-lingual Classification
with Machine Translation
Semantics add little/no
benefit when building, and
applying, classification
models on the same
language
0.831
0.709
0.781
0.774
0.818
0.712
0.776
0.769
English [English] Italian [Italian] Spanish [Spanish] average
Train language [Test language]
Statistical Features
Semantic Features
Monolingual Classification
with Monolingual Models
39. Task 1 Crisis vs. non-Crisis Related Messages
Task 2 Type of Crisis
Task 3 Type of Information
Differentiate those posts that are related to a crisis
situation vs. those posts that are not
Identify the different types of crises the message is
related to
Differentiate those posts that are related to a crisis
situation vs. those posts that are not
Granularity CRISIS-DATA PROCESSING TASKS
Shooting, Explosion, Building Collapse, Fires,
Floods, Meteorite Fall, etc.
Affected Individuals, Infrastructures and Utilities,
Donations and Volunteer, Caution and Advice,
etc.
Olteanu, A., Vieweg, S., Castillo, C. What to Expect When
the Unexpected Happens: Social Media Communications
Across Crises. ACM Comp. Supported Cooperative Work
and Social Computing (CSCW), 2015
44. DEEP LEARNING FOR CRISIS EVENT DETECTION
A semantically-enriched deep learning
model for event detection on Twitter
Tweets Preprocessing
Concept
Extraction
Word
Vectors
Initialisation
Sem-CNN
Training
Pre-trained
Embeddings
Semantic
Vectors
Initialisation
Bag of Words
Bag of Concepts
T = “Obama
attends vigil for
Boston Marathon
bombing victims”
W = [obama, attends, vigil, for, boston,
marathon, bombing, victims]
C = [obama, politician, none, none,
none, boston, location, none, none,
none]
Term-Document Vector
(Term Presence)
Embeddings
obama
politician
boston
location
...
...
...
...
none
obama
attends
vigil
for
boston
marathon
bombing
victims
1
1
1
1
0
0
0
0
1
Concepts
Vector
DEEP LEARNING
MODEL
Affected Individuals, Infrastructures and Utilities, Donations and Volunteering, Caution and
Advice, Sympathy and Support, Other Useful Information (Olteanu et al 2015)
45. CLASSIFYING TWEETS WITH DEEP LEARNING
SVM (TF-IDF): A linear kernel SVM
classifier trained from the words’ TF-
IDF vectors extracted from our dataset
SVM (Word2Vec): A linear kernel SVM
classifier trained from the Google pre-
trained 300-dimensional word
embeddings
SEM-DL: Semantic Deep Learning
approach
Data is from CrisisLexT26: 26 crisis events,
with 1,000 annotated tweets for a total of
around 28,000 tweets. Data is too small for
Deep Learning, hence only a proof of concept
0.48 0.5 0.52 0.54 0.56 0.58 0.6 0.62 0.64
Precision
Recall
F1
SEM-DL SVM (Word2Vec) SVM (TF-IDF)
Burel, G.; Saif, H. and Alani, H. Semantic Wide and Deep Learning for Detecting Crisis-Information Categories on Social Media. Int. Semantic Web Conf. (ISWC), Vienna, Austria, 2017.
46. CREES automatically processes short texts in a Google sheet, and
identifies if a text is about a crisis, crisis-types and information-types
Uses Deep Learning methods
Google Sheet Add-on
CRISIS EVENT EXTRACTION SERVICE
Burel, G. & Alani, H. Crisis Event Extraction Service (CREES) - Automatic Detection and Classification of Crisis-related Content on Social Media. 15th Int. Conf. on Info. Sys. for Crisis Response and Management, Rochester, NY, USA, 2018
53. DEEP LEARNING
RUMOUR
VERACITY
CLASSIFIER
Can work without waiting for responses
(e.g., comments, retweets)
https://cloud.gate.ac.uk/shopfront/displayItem/rumour-veracity
Does not require the reactions
(stances) given by the responses --
stance detection may introduce noise
Makes use only of the source tweet
54. CHATBOTS FOR
CRISES REPORTING
Potential vs Reality
On FB Messenger alone, there are currently
over 300K active bots, exchanging over 8
billion messages between people and
businesses each month.
57. What kind of issue would you like to
report?
Good afternoon first of all
Oh my, I'm not programmed to
understand what you're saying. Sorry!
CHATBOTS – A LONG WAY TO GO
Visits to the Facebook chatbot
Visitors who clicked around in chatbot
Users not following user flow
Users tried to follow user flow
Technical fault when submitting
Reports successfully sent to Uchaguzi
Total reports submitted through Twitter, SMS,
onsite reporters
Reports structured, geolocated, verified, and
published
6875
687
3034
1501
1150
222
106
55
CHATBOT
STATS
PLATFORM
STATS
65%
35%
CHATBOT USER DEMOGRAPHICS
58.
59. WHAT’S NEXT
Inclusiveness of social media
Biases: gender, technology, social media platform, language
Usage of social media can differ across countries, cultures,
genders, platforms, economies …
How can we encourage, and direct, a better and more
sustained crowdsourcing during disasters
Many tools and services: when and how they need to be
orchestrated and used
Relevancy and value of social media crisis data is subjective
and person/time dependent
60. Free, A.I. powered tools are now
available, to:
• Separate relevant from rubbish
tweets, in ”multiple languages”, and
for “any” type of crisis
• Identify the category of crisis
information they hold
• Measure their veracity
”.. I would suggest, then, that the formula for
the next 10,000 start-ups is very, very simple,
which is to take x and add AI. That is the
formula, that's what we're going to be doing.
And that is the way in which we're going to
make this second Industrial Revolution”
Kevin Kelly, IBM