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Amparo Elizabeth Cano Basave
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This is a presentation I gave in Bristol describing the DiggiCORE project and the challenges it addresses.
DiggiCORE: Digging into Connected Repositories
DiggiCORE: Digging into Connected Repositories
petrknoth
CORE projects family
CORE projects family
petrknoth
DEVCSI Core Mobile
DEVCSI Core Mobile
petrknoth
OR2012 presentation on Text Mining in CORE
Text mining in CORE (OR2012)
Text mining in CORE (OR2012)
petrknoth
Pcst2014 salvador
Pcst2014 salvador
Alexandra Okada
Knowledge acquisition from Social Networking Sites. Liberate the Potential for Developing Intelligent Applications. (First session of ekaw KASNA tutorial). Oct 15 2010. More info at http://oak.dcs.shef.ac.uk/ekaw_2010_ka_from_sna_tutorial/
Ekaw2010 tutorial3
Ekaw2010 tutorial3
Amparo Elizabeth Cano Basave
Does sizematter
Does sizematter
Amparo Elizabeth Cano Basave
Persuasive communication is the process of shaping, reinforcing and changing others’ responses. In political debates, speakers ex- press their views towards the debated topics by choosing both the content of their discourse and the argumentation process. In this work we study the use of semantic frames for modelling argumentation in speakers’ discourse. We investigate the impact of a speaker’s argumentation style and their effect in influencing an audience in supporting their candidature. We model the influence index of each candidate based on their relative standings in the polls released prior to the debate and present a system which ranks speakers in terms of their relative influence using a combination of content and persuasive argumentation features. Our results show that although con- tent alone is predictive of a speaker’s influence rank, persuasive argumentation also affects such indices.
A Study of the Impact of Persuasive Argumentation in Political Debates
A Study of the Impact of Persuasive Argumentation in Political Debates
Amparo Elizabeth Cano Basave
Empfohlen
This is a presentation I gave in Bristol describing the DiggiCORE project and the challenges it addresses.
DiggiCORE: Digging into Connected Repositories
DiggiCORE: Digging into Connected Repositories
petrknoth
CORE projects family
CORE projects family
petrknoth
DEVCSI Core Mobile
DEVCSI Core Mobile
petrknoth
OR2012 presentation on Text Mining in CORE
Text mining in CORE (OR2012)
Text mining in CORE (OR2012)
petrknoth
Pcst2014 salvador
Pcst2014 salvador
Alexandra Okada
Knowledge acquisition from Social Networking Sites. Liberate the Potential for Developing Intelligent Applications. (First session of ekaw KASNA tutorial). Oct 15 2010. More info at http://oak.dcs.shef.ac.uk/ekaw_2010_ka_from_sna_tutorial/
Ekaw2010 tutorial3
Ekaw2010 tutorial3
Amparo Elizabeth Cano Basave
Does sizematter
Does sizematter
Amparo Elizabeth Cano Basave
Persuasive communication is the process of shaping, reinforcing and changing others’ responses. In political debates, speakers ex- press their views towards the debated topics by choosing both the content of their discourse and the argumentation process. In this work we study the use of semantic frames for modelling argumentation in speakers’ discourse. We investigate the impact of a speaker’s argumentation style and their effect in influencing an audience in supporting their candidature. We model the influence index of each candidate based on their relative standings in the polls released prior to the debate and present a system which ranks speakers in terms of their relative influence using a combination of content and persuasive argumentation features. Our results show that although con- tent alone is predictive of a speaker’s influence rank, persuasive argumentation also affects such indices.
A Study of the Impact of Persuasive Argumentation in Political Debates
A Study of the Impact of Persuasive Argumentation in Political Debates
Amparo Elizabeth Cano Basave
Online paedophile activity in social media has become a major concern in society as Internet access is easily available to a broader younger population. One common form of online child exploitation is child grooming, where adults and minors exchange sexual text and media via social media platforms. Such behaviour involves a number of stages performed by a predator (adult) with the final goal of approaching a victim (minor) in person. This paper presents a study of such online grooming stages from a machine learning perspective. We propose to characterise such stages by a series of features covering sentiment polarity, content, and psycho-linguistic and discourse patterns. Our experiments with online chatroom conversations show good results in automatically classifying chatlines into various grooming stages. Such a deeper understanding and tracking of predatory behaviour is vital for building robust systems for detecting grooming conversations and potential predators on social media.
Detecting child grooming behaviour patterns on social media
Detecting child grooming behaviour patterns on social media
Amparo Elizabeth Cano Basave
Stretching the Life of Twitter Classifiers with Time-Stamped Semantic Graphs
Stretching the Life of Twitter Classifiers with Time-Stamped Semantic Graphs
Stretching the Life of Twitter Classifiers with Time-Stamped Semantic Graphs
Amparo Elizabeth Cano Basave
Presentation for the paper entitled: "A Weakly Supervised Bayesian Model for Violence Detection in Social Media" presented at the IJCNLP 2013
Violence det ijcnlp13-slideshare
Violence det ijcnlp13-slideshare
Amparo Elizabeth Cano Basave
Presented at Hypertext'13. Topic classification (TC) of short text messages o↵ers an ef- fective and fast way to reveal events happening around the world ranging from those related to Disaster (e.g. Sandy hurricane) to those related to Violence (e.g. Egypt revolu- tion). Previous approaches to TC have mostly focused on exploiting individual knowledge sources (KS) (e.g. DBpedia or Freebase) without considering the graph structures that surround concepts present in KSs when detecting the top- ics of Tweets. In this paper we introduce a novel approach for harnessing such graph structures from multiple linked KSs, by: (i) building a conceptual representation of the KSs, (ii) leveraging contextual information about concepts by exploiting semantic concept graphs, and (iii) providing a principled way for the combination of KSs. Experiments evaluating our TC classifier in the context of Violence detec- tion (VD) and Emergency Responses (ER) show promising results that significantly outperform various baseline models including an approach using a single KS without linked data and an approach using only Tweets.
Harnessing Linked Knowledge Sources for Topic Classification in Social Media
Harnessing Linked Knowledge Sources for Topic Classification in Social Media
Amparo Elizabeth Cano Basave
Location sharing services(LSS) like Foursquare, Gowalla and Face- book Places gather information from millions of users who leave trails in loca- tions (i.e. chekins) in the form of micro-posts. These footprints provide a unique opportunity to explore the way in which users engage and perceive a point of interest (POI). A POI is as a human construct which describes information about locations (e.g restaurants, cities). In this work we investigate whether the collec- tive perception of a POI can be used as a real-time dataset from which POI’s transient features can be extracted. We introduce a graph-based model for profil- ing geographical areas based on social awareness streams. Based on this model we define a set of measures that can characterise a location-based social aware- ness stream as well as act as indicators of volatile events occurring at a POI. We applied the model and measures on a dataset consisting of a collection of tweets generated at the city of Sheffield and registered over three week-ends. The model and measures introduced in this paper are relevant for design of future location-based services, real-time emergency-response models, as well as traffic forecasting. Our empirical findings demonstrate that social awareness streams not only can act as an event-sensor but also can enrich the profile of a location-entity.
Volatile Classification of Point of Interests based on Social Activity Streams
Volatile Classification of Point of Interests based on Social Activity Streams
Amparo Elizabeth Cano Basave
Sensor Networks meets Social Web
Sensing Presence (PreSense) Ontology – User Modelling in the Semantic ...
Sensing Presence (PreSense) Ontology – User Modelling in the Semantic ...
Amparo Elizabeth Cano Basave
Topica
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Amparo Elizabeth Cano Basave
Entity-Based Semantics Emerging from Personal Awareness Streams
Entity-Based Semantics Emerging from Personal Awareness Streams
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Knowledge Acquisition fro
Ekaw2010 tutorial3 practical
Ekaw2010 tutorial3 practical
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We present a methodology for asserting r
Representing, Proving and Sharing Trustworthiness of Web Resources Using Vera...
Representing, Proving and Sharing Trustworthiness of Web Resources Using Vera...
Amparo Elizabeth Cano Basave
Veracity- Modeling and Proving Trustworthiness of Web Resources
Veracity- Modeling and Proving Trustworthiness of Web Resources
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Online paedophile activity in social media has become a major concern in society as Internet access is easily available to a broader younger population. One common form of online child exploitation is child grooming, where adults and minors exchange sexual text and media via social media platforms. Such behaviour involves a number of stages performed by a predator (adult) with the final goal of approaching a victim (minor) in person. This paper presents a study of such online grooming stages from a machine learning perspective. We propose to characterise such stages by a series of features covering sentiment polarity, content, and psycho-linguistic and discourse patterns. Our experiments with online chatroom conversations show good results in automatically classifying chatlines into various grooming stages. Such a deeper understanding and tracking of predatory behaviour is vital for building robust systems for detecting grooming conversations and potential predators on social media.
Detecting child grooming behaviour patterns on social media
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Amparo Elizabeth Cano Basave
Stretching the Life of Twitter Classifiers with Time-Stamped Semantic Graphs
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Amparo Elizabeth Cano Basave
Presentation for the paper entitled: "A Weakly Supervised Bayesian Model for Violence Detection in Social Media" presented at the IJCNLP 2013
Violence det ijcnlp13-slideshare
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Amparo Elizabeth Cano Basave
Presented at Hypertext'13. Topic classification (TC) of short text messages o↵ers an ef- fective and fast way to reveal events happening around the world ranging from those related to Disaster (e.g. Sandy hurricane) to those related to Violence (e.g. Egypt revolu- tion). Previous approaches to TC have mostly focused on exploiting individual knowledge sources (KS) (e.g. DBpedia or Freebase) without considering the graph structures that surround concepts present in KSs when detecting the top- ics of Tweets. In this paper we introduce a novel approach for harnessing such graph structures from multiple linked KSs, by: (i) building a conceptual representation of the KSs, (ii) leveraging contextual information about concepts by exploiting semantic concept graphs, and (iii) providing a principled way for the combination of KSs. Experiments evaluating our TC classifier in the context of Violence detec- tion (VD) and Emergency Responses (ER) show promising results that significantly outperform various baseline models including an approach using a single KS without linked data and an approach using only Tweets.
Harnessing Linked Knowledge Sources for Topic Classification in Social Media
Harnessing Linked Knowledge Sources for Topic Classification in Social Media
Amparo Elizabeth Cano Basave
Location sharing services(LSS) like Foursquare, Gowalla and Face- book Places gather information from millions of users who leave trails in loca- tions (i.e. chekins) in the form of micro-posts. These footprints provide a unique opportunity to explore the way in which users engage and perceive a point of interest (POI). A POI is as a human construct which describes information about locations (e.g restaurants, cities). In this work we investigate whether the collec- tive perception of a POI can be used as a real-time dataset from which POI’s transient features can be extracted. We introduce a graph-based model for profil- ing geographical areas based on social awareness streams. Based on this model we define a set of measures that can characterise a location-based social aware- ness stream as well as act as indicators of volatile events occurring at a POI. We applied the model and measures on a dataset consisting of a collection of tweets generated at the city of Sheffield and registered over three week-ends. The model and measures introduced in this paper are relevant for design of future location-based services, real-time emergency-response models, as well as traffic forecasting. Our empirical findings demonstrate that social awareness streams not only can act as an event-sensor but also can enrich the profile of a location-entity.
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Sensing Presence (PreSense) Ontology – User Modelling in the Semantic ...
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Detecting child grooming behaviour patterns on social media
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Stretching the Life of Twitter Classifiers with Time-Stamped Semantic Graphs
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Violence det ijcnlp13-slideshare
Harnessing Linked Knowledge Sources for Topic Classification in Social Media
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