Semantic Grounding Strategies for Tagbased Recommender Systems dannyijwest
Recommender systems usually operate on similarities between recommended items or users. Tag based
recommender systems utilize similarities on tags. The tags are however mostly free user entered phrases.
Therefore, similarities computed without their semantic groundings might lead to less relevant
recommendations. In this paper, we study a semantic grounding used for tag similarity calculus. We show a
comprehensive analysis of semantic grounding given by 20 ontologies from different domains. The study
besides other things reveals that currently available OWL ontologies are very narrow and the percentage
of the similarity expansions is rather small. WordNet scores slightly better as it is broader but not much as
it does not support several semantic relationships. Furthermore, the study reveals that even with such
number of expansions, the recommendations change considerably.
Stop thinking, start tagging - Tag Semantics emerge from Collaborative VerbosityInovex GmbH
Recent research provides evidence for the presence of emergent semantics in collaborative tagging systems. While several methods have been proposed, little is known about the factors that influence the evolution of semantic structures in these systems. A natural hypothesis is that the quality of the emergent semantics depends on the pragmatics of tagging: Users with certain usage patterns might contribute more to the resulting semantics than others. In this work, we propose several measures which enable a pragmatic differentiation of taggers by their degree of contribution to emerging semantic structures. We distinguish between categorizers, who typically use a small set of tags as a replacement for hierarchical classification schemes, and describers, who are annotating resources with a wealth of freely associated, descriptive keywords. To study our hypothesis, we apply semantic similarity measures to 64 different partitions of real-world and large-scale folksonomy containing different ratios of categorizers and describers. Our results not only show that ‘verbose’ taggers are most useful for the emergence of tag semantics, but also that a subset containing only 40% of the most ‘verbose’ taggers can produce results that match and even outperform the semantic precision obtained from the whole dataset. Moreover, the results suggest that there exists a causal link between the pragmatics of tagging and resulting emergent semantics. This work is relevant for designers and analysts of tagging systems interested (i) in fostering the semantic development of their platforms, (ii) in identifying users introducing “semantic noise”, and (iii) in learning ontologies.
UNIT V TEXT AND OPINION MINING
Text Mining in Social Networks -Opinion extraction – Sentiment classification and clustering -
Temporal sentiment analysis - Irony detection in opinion mining - Wish analysis – Product review mining – Review Classification – Tracking sentiments towards topics over time
Prediction of Reaction towards Textual Posts in Social NetworksMohamed El-Geish
Posting on social networks could be a gratifying or a terrifying experience depending on the reaction the post and its author —by association— receive from the readers. To better understand what makes a post popular, this project inquires into the factors that determine the number of likes, comments, and shares a textual post gets on LinkedIn; and finds a predictor function that can estimate those quantitative social gestures.
Evolving Swings (topics) from Social Streams using Probability ModelIJERA Editor
Evolving swings from social streams is receiving renewed interest and it is motivated by the growth of social
media and social streams. Non-conventional based approaches can be appropriate which include text, images,
URLs and videos. The focus is on evolving topics by social aspects of the networks and the mentions of user
links between users which are generated intentionally or unintentionally through replies, mentions and retweets.
A probability model of the mentioning behavior is proposed and the proposed model detects the evolving topic
from the anomalies measured. After a several experiments, it shows that mention anomaly based approaches
detects the evolving swing as early as text anomaly based approa0ches.
A Proposal on Social Tagging Systems Using Tensor Reduction and Controlling R...ijcsa
Social Tagging System is the process in which user makes their interest by tagging on a particular item. These STS are in associated with web 2.0 and has sourceful information for the users with their recommendations. It provides different types of recommendations are modeled by a 3-order tensor, on which multiway latent semantic analysis and dimensionality reduction is performed using both the Higher Order Singular Value Decomposition (HOSVD) method and the KernelSVD smoothing technique. We provide now with the 4-order tensor approach, which we named as Tensor Reduction. Here the items that are tagged can be viewed by the user who are recommended the same item and tagged over it. There by can improve the social tagging recommendations efficiency and also the unwanted request has been controlled. The results show significant improvements in terms of effectiveness.
Semantic Grounding Strategies for Tagbased Recommender Systems dannyijwest
Recommender systems usually operate on similarities between recommended items or users. Tag based
recommender systems utilize similarities on tags. The tags are however mostly free user entered phrases.
Therefore, similarities computed without their semantic groundings might lead to less relevant
recommendations. In this paper, we study a semantic grounding used for tag similarity calculus. We show a
comprehensive analysis of semantic grounding given by 20 ontologies from different domains. The study
besides other things reveals that currently available OWL ontologies are very narrow and the percentage
of the similarity expansions is rather small. WordNet scores slightly better as it is broader but not much as
it does not support several semantic relationships. Furthermore, the study reveals that even with such
number of expansions, the recommendations change considerably.
Stop thinking, start tagging - Tag Semantics emerge from Collaborative VerbosityInovex GmbH
Recent research provides evidence for the presence of emergent semantics in collaborative tagging systems. While several methods have been proposed, little is known about the factors that influence the evolution of semantic structures in these systems. A natural hypothesis is that the quality of the emergent semantics depends on the pragmatics of tagging: Users with certain usage patterns might contribute more to the resulting semantics than others. In this work, we propose several measures which enable a pragmatic differentiation of taggers by their degree of contribution to emerging semantic structures. We distinguish between categorizers, who typically use a small set of tags as a replacement for hierarchical classification schemes, and describers, who are annotating resources with a wealth of freely associated, descriptive keywords. To study our hypothesis, we apply semantic similarity measures to 64 different partitions of real-world and large-scale folksonomy containing different ratios of categorizers and describers. Our results not only show that ‘verbose’ taggers are most useful for the emergence of tag semantics, but also that a subset containing only 40% of the most ‘verbose’ taggers can produce results that match and even outperform the semantic precision obtained from the whole dataset. Moreover, the results suggest that there exists a causal link between the pragmatics of tagging and resulting emergent semantics. This work is relevant for designers and analysts of tagging systems interested (i) in fostering the semantic development of their platforms, (ii) in identifying users introducing “semantic noise”, and (iii) in learning ontologies.
UNIT V TEXT AND OPINION MINING
Text Mining in Social Networks -Opinion extraction – Sentiment classification and clustering -
Temporal sentiment analysis - Irony detection in opinion mining - Wish analysis – Product review mining – Review Classification – Tracking sentiments towards topics over time
Prediction of Reaction towards Textual Posts in Social NetworksMohamed El-Geish
Posting on social networks could be a gratifying or a terrifying experience depending on the reaction the post and its author —by association— receive from the readers. To better understand what makes a post popular, this project inquires into the factors that determine the number of likes, comments, and shares a textual post gets on LinkedIn; and finds a predictor function that can estimate those quantitative social gestures.
Evolving Swings (topics) from Social Streams using Probability ModelIJERA Editor
Evolving swings from social streams is receiving renewed interest and it is motivated by the growth of social
media and social streams. Non-conventional based approaches can be appropriate which include text, images,
URLs and videos. The focus is on evolving topics by social aspects of the networks and the mentions of user
links between users which are generated intentionally or unintentionally through replies, mentions and retweets.
A probability model of the mentioning behavior is proposed and the proposed model detects the evolving topic
from the anomalies measured. After a several experiments, it shows that mention anomaly based approaches
detects the evolving swing as early as text anomaly based approa0ches.
A Proposal on Social Tagging Systems Using Tensor Reduction and Controlling R...ijcsa
Social Tagging System is the process in which user makes their interest by tagging on a particular item. These STS are in associated with web 2.0 and has sourceful information for the users with their recommendations. It provides different types of recommendations are modeled by a 3-order tensor, on which multiway latent semantic analysis and dimensionality reduction is performed using both the Higher Order Singular Value Decomposition (HOSVD) method and the KernelSVD smoothing technique. We provide now with the 4-order tensor approach, which we named as Tensor Reduction. Here the items that are tagged can be viewed by the user who are recommended the same item and tagged over it. There by can improve the social tagging recommendations efficiency and also the unwanted request has been controlled. The results show significant improvements in terms of effectiveness.
Fuzzy AndANN Based Mining Approach Testing For Social Network AnalysisIJERA Editor
Fast and Appropriate Social Network Analysis (SNA) tools ,techniques, are required to collect and classify
opinion scores on social networksites , as a grouping on wrong opinion may create problems for a society or
country . Social Network Analysis (SNA) is popular means for researcher as the number of users and groups
increasing day by day on that social sites , and a large group may influence other.In this paper, we
recommendhybrid model of opinion recommendation systems, for single user and for collective community
respectively, formed on social liking and influence network theory. By collecting thedata of user social networks
and preferenceslike, we designed aimproved hybrid prototype to imitate the social influence by like and sharing
the information among groups.The significance of this paper to analyze the suitability of ANN and Fuzzy sets
method in a hybrid manner for social web sites classifications, First, we intend to use Artificial Neural
Network(ANN)techniques in social media data classification by using some contemporary methods different
than the conventional methods of statistics and data analysis, in next we want to propagate the fuzzy approach
as a way to overcome the uncertainity that is always present in social media analysis . We give a brief overview
of the main ideas and recent results of social networks analysis , and we point to relationships between the two
social network analysis and classification approaches .This researchsuggests a hybrid classification model build
on fuzzy and artificial neural network (HFANN). Information Gain and three popular social sites are used to
collect information depicting features that are then used to train and test the proposed methods . This neoteric
approach combines the advantages of ANN and Fuzzy sets in classification accuracy with utilizing social data
and knowledge base available in the hate lexicons.
Hate speech has been an ongoing problem on the Internet for many years. Besides, social media, especially Facebook, and Twitter have given it a global stage where those hate speeches can spread far more rapidly. Every social media platform needs to implement an effective hate speech detection system to remove offensive content in real-time. There are various approaches to identify hate speech, such as Rule-Based, Machine Learning based, deep learning based and Hybrid approach. Since this is a review paper, we explained the valuable works of various authors who have invested their valuable time in studying to identifying hate speech using various approaches.
Identification of inference attacks on private Information from Social Networkseditorjournal
Online social networks, like
Facebook, twitter are increasingly utilized by
many people. These networks permit users to
publish details about them and to connect to
their friends. Some of the details revealed
inside these networks are meant to be
keeping private. Yet it is possible to use
learning algorithms and methods on released
data have to predict private information,
which cause inference attacks. This paper
discovers how to launch inference attacks
using released social networking details to
predict private information’s. It then
separate three possible sanitization
algorithms that could be used in various
situations. Then, it investigates the
effectiveness of these techniques and tries to
use methods of collective inference
techniques to determine sensitive attributes
of the user data set. It shows that it can
decline the effectiveness of both the local and
relational classification algorithms by using
the sanitization methods we described.
In the age of social media communication, it is easy to
modulate the minds of users and also instigate violent
actions being taken by them in some cases. There is a need
to have a system that can analyze the threat level of tweets
from influential users and rank their Twitter handles so
that dangerous tweets can be avoided going public on
Twitter before fact-checking which can hurt the sentiments
of people and can take the shape of violence. The study
aims to analyse and rank twitter users according to their
influential power and extremism of their tweets to help
prevent major protests and violent events. We scraped top
trending topics and fetched tweets using those hashtags.
We propose a custom ranking algorithm which considers
source based and content based features along with a
knowledge graph which generates the score and rank the
twitter users according to the scores. Our aim with this
study is to identify and rank extremist twitter users with
regards to their impact and influence. We use a technique
that takes into consideration both source based and
content-based features of tweets to generate the ranking of
the extremist twitter users having a high impact factor
Political prediction analysis using text mining and deep learningVishwambhar Deshpande
We have proposed a system to determine current sentiment on twitter using Twit-
ter API for open access which includes opinions from dierent content structures like
latest news, audits, articles and social media posts. and Deep Learning method to
study Historic Data for predicting future results. we utilized Naive Bayes and dictio-
nary based algorithms to predict the sentiment on Live Twitter Data.
CATEGORIZING 2019-N-COV TWITTER HASHTAG DATA BY CLUSTERINGijaia
Unsupervised machine learning techniques such as clustering are widely gaining use with the recent increase in social communication platforms like Twitter and Facebook. Clustering enables the finding of patterns in these unstructured datasets. We collected tweets matching hashtags linked to COVID-19 from a Kaggle dataset. We compared the performance of nine clustering algorithms using this dataset. We evaluated the generalizability of these algorithms using a supervised learning model. Finally, using a selected unsupervised learning algorithm we categorized the clusters. The top five categories are Safety,
Crime, Products, Countries and Health. This can prove helpful for bodies using large amount of Twitter data needing to quickly find key points in the data before going into further classification.
This presentation compares 3 educational tagging systems and their tags, and shows that tags from one system are of interest to users of the other, hence the idea of a cross-repository tag cloud. The papers are here: http://CEUR-WS.org/Vol-382/
The use of social tagging to support the cataloguing of learning objectsLuciana Zaina
Social networks have become the main media for information dissemination in the so-called Web 2.0. The core of these networks is social tagging, the act of annotating what users see in their social space. In the education domain, social tagging is potentially a useful resource to improve the organization (cataloguing) of large repositories of learning objects. To the present moment, however, many questions are open about social tagging in e-learning. In this work, hence, we proceed to answer
three questions: (1) Can social tagging successfully catalog e-learning objects? (2) How do students behave according to Korner’s classification: categorizers or describers? and (3) Does ¨social tagging converge to a well-defined descriptive vocabulary of tags? We performed a large experiment with 336 technician students that marked 218 electronic learning objects for about 4,985 times. Our results show that social tagging is a promising practice for e-learning; however some issues must be addressed to
prevent an excessive number of categorizer students and, also, a premature convergence of the vocabulary of tags. Our conclusions are specific for the setting of our experiment, but we generalize them as much as possible suggesting guidelines of how to use social tagging in e-learning.
Fuzzy AndANN Based Mining Approach Testing For Social Network AnalysisIJERA Editor
Fast and Appropriate Social Network Analysis (SNA) tools ,techniques, are required to collect and classify
opinion scores on social networksites , as a grouping on wrong opinion may create problems for a society or
country . Social Network Analysis (SNA) is popular means for researcher as the number of users and groups
increasing day by day on that social sites , and a large group may influence other.In this paper, we
recommendhybrid model of opinion recommendation systems, for single user and for collective community
respectively, formed on social liking and influence network theory. By collecting thedata of user social networks
and preferenceslike, we designed aimproved hybrid prototype to imitate the social influence by like and sharing
the information among groups.The significance of this paper to analyze the suitability of ANN and Fuzzy sets
method in a hybrid manner for social web sites classifications, First, we intend to use Artificial Neural
Network(ANN)techniques in social media data classification by using some contemporary methods different
than the conventional methods of statistics and data analysis, in next we want to propagate the fuzzy approach
as a way to overcome the uncertainity that is always present in social media analysis . We give a brief overview
of the main ideas and recent results of social networks analysis , and we point to relationships between the two
social network analysis and classification approaches .This researchsuggests a hybrid classification model build
on fuzzy and artificial neural network (HFANN). Information Gain and three popular social sites are used to
collect information depicting features that are then used to train and test the proposed methods . This neoteric
approach combines the advantages of ANN and Fuzzy sets in classification accuracy with utilizing social data
and knowledge base available in the hate lexicons.
Hate speech has been an ongoing problem on the Internet for many years. Besides, social media, especially Facebook, and Twitter have given it a global stage where those hate speeches can spread far more rapidly. Every social media platform needs to implement an effective hate speech detection system to remove offensive content in real-time. There are various approaches to identify hate speech, such as Rule-Based, Machine Learning based, deep learning based and Hybrid approach. Since this is a review paper, we explained the valuable works of various authors who have invested their valuable time in studying to identifying hate speech using various approaches.
Identification of inference attacks on private Information from Social Networkseditorjournal
Online social networks, like
Facebook, twitter are increasingly utilized by
many people. These networks permit users to
publish details about them and to connect to
their friends. Some of the details revealed
inside these networks are meant to be
keeping private. Yet it is possible to use
learning algorithms and methods on released
data have to predict private information,
which cause inference attacks. This paper
discovers how to launch inference attacks
using released social networking details to
predict private information’s. It then
separate three possible sanitization
algorithms that could be used in various
situations. Then, it investigates the
effectiveness of these techniques and tries to
use methods of collective inference
techniques to determine sensitive attributes
of the user data set. It shows that it can
decline the effectiveness of both the local and
relational classification algorithms by using
the sanitization methods we described.
In the age of social media communication, it is easy to
modulate the minds of users and also instigate violent
actions being taken by them in some cases. There is a need
to have a system that can analyze the threat level of tweets
from influential users and rank their Twitter handles so
that dangerous tweets can be avoided going public on
Twitter before fact-checking which can hurt the sentiments
of people and can take the shape of violence. The study
aims to analyse and rank twitter users according to their
influential power and extremism of their tweets to help
prevent major protests and violent events. We scraped top
trending topics and fetched tweets using those hashtags.
We propose a custom ranking algorithm which considers
source based and content based features along with a
knowledge graph which generates the score and rank the
twitter users according to the scores. Our aim with this
study is to identify and rank extremist twitter users with
regards to their impact and influence. We use a technique
that takes into consideration both source based and
content-based features of tweets to generate the ranking of
the extremist twitter users having a high impact factor
Political prediction analysis using text mining and deep learningVishwambhar Deshpande
We have proposed a system to determine current sentiment on twitter using Twit-
ter API for open access which includes opinions from dierent content structures like
latest news, audits, articles and social media posts. and Deep Learning method to
study Historic Data for predicting future results. we utilized Naive Bayes and dictio-
nary based algorithms to predict the sentiment on Live Twitter Data.
CATEGORIZING 2019-N-COV TWITTER HASHTAG DATA BY CLUSTERINGijaia
Unsupervised machine learning techniques such as clustering are widely gaining use with the recent increase in social communication platforms like Twitter and Facebook. Clustering enables the finding of patterns in these unstructured datasets. We collected tweets matching hashtags linked to COVID-19 from a Kaggle dataset. We compared the performance of nine clustering algorithms using this dataset. We evaluated the generalizability of these algorithms using a supervised learning model. Finally, using a selected unsupervised learning algorithm we categorized the clusters. The top five categories are Safety,
Crime, Products, Countries and Health. This can prove helpful for bodies using large amount of Twitter data needing to quickly find key points in the data before going into further classification.
This presentation compares 3 educational tagging systems and their tags, and shows that tags from one system are of interest to users of the other, hence the idea of a cross-repository tag cloud. The papers are here: http://CEUR-WS.org/Vol-382/
The use of social tagging to support the cataloguing of learning objectsLuciana Zaina
Social networks have become the main media for information dissemination in the so-called Web 2.0. The core of these networks is social tagging, the act of annotating what users see in their social space. In the education domain, social tagging is potentially a useful resource to improve the organization (cataloguing) of large repositories of learning objects. To the present moment, however, many questions are open about social tagging in e-learning. In this work, hence, we proceed to answer
three questions: (1) Can social tagging successfully catalog e-learning objects? (2) How do students behave according to Korner’s classification: categorizers or describers? and (3) Does ¨social tagging converge to a well-defined descriptive vocabulary of tags? We performed a large experiment with 336 technician students that marked 218 electronic learning objects for about 4,985 times. Our results show that social tagging is a promising practice for e-learning; however some issues must be addressed to
prevent an excessive number of categorizer students and, also, a premature convergence of the vocabulary of tags. Our conclusions are specific for the setting of our experiment, but we generalize them as much as possible suggesting guidelines of how to use social tagging in e-learning.
A Survey on Decision Support Systems in Social MediaEditor IJCATR
Web 3.0 is the upcoming phase in web evolution. Web 3.0 will be about “feeding you the information that you want, when
you want it” i.e. personalization of the web. In web 3.0 the basic principle is linking, integrating and analyzing data from various data
sources into new information streams by means of semantic technology. So, we can say that Web 3.0 comprises of two platforms
semantic technologies and social computing environment. Recommender system is a subclass of decision support system.
Recommendations in social web are used to personalize the web [20]. Social Tagging System is one type of social media. In this
paper we present the survey of various recommendations in Social Tagging Systems (STSs) like tag, item, user and unified
recommendations along with semantic web and also discussed about major thrust areas of research in each category.
A Survey on Decision Support Systems in Social MediaEditor IJCATR
Web 3.0 is the upcoming phase in web evolution. Web 3.0 will be about “feeding you the information that you want, when you want it” i.e. personalization of the web. In web 3.0 the basic principle is linking, integrating and analyzing data from various data sources into new information streams by means of semantic technology. So, we can say that Web 3.0 comprises of two platforms semantic technologies and social computing environment. Recommender system is a subclass of decision support system. Recommendations in social web are used to personalize the web [20]. Social Tagging System is one type of social media. In this paper we present the survey of various recommendations in Social Tagging Systems (STSs) like tag, item, user and unified recommendations along with semantic web and also discussed about major thrust areas of research in each category.
A Survey on Decision Support Systems in Social MediaEditor IJCATR
Web 3.0 is the upcoming phase in web evolution. Web 3.0 will be about “feeding you the information that you want, when
you want it” i.e. personalization of the web. In web 3.0 the basic principle is linking, integrating and analyzing data from various data
sources into new information streams by means of semantic technology. So, we can say that Web 3.0 comprises of two platforms
semantic technologies and social computing environment. Recommender system is a subclass of decision support system.
Recommendations in social web are used to personalize the web [20]. Social Tagging System is one type of social media. In this
paper we present the survey of various recommendations in Social Tagging Systems (STSs) like tag, item, user and unified
recommendations along with semantic web and also discussed about major thrust areas of research in each category.
LAK2011: 1st International Conference on Learning Analytics and Knowledge February 27-March 1, 2011
Banff, Alberta
Anna De Liddo, Simon Buckingham Shum,
Ivana Quinto, Michelle Bachler, Lorella Cannavacciuolo
Social bookmarking system is a web-based resource sharing system that allows users to upload, share and
organize their resources i.e. bookmarks and publications. The system has shifted the paradigm of
bookmarking from an individual activity limited to desktop to a collective activity on the web. It also
facilitates user to annotate his resource with free form tags that leads to large communities of users to
collaboratively create accessible repositories of web resources. Tagging process has its own challenges
like ambiguity, redundancy or misspelled tags and sometimes user tends to avoid it as he has to describe
tag at his own. The resultant tag space is noisy or very sparse and dilutes the purpose of tagging. The
effective solution is Tag Recommendation System that automatically suggests appropriate set of tags to
user while annotating resource. In this paper, we propose a framework that does not depend on tagging
history of the resource or user and thereby capable of suggesting tags to the resources which are being
submitted to the system first time. We model tag recommendation task as multi-label text classification
problem and use Naive Bayes classifier as the base learner of the multilabel classifier. We experiment with
Boolean, bag-of-words and term frequency-inverse document frequency (TFIDF) representation of the
resources and fit appropriate distribution to the data based on the representation used. Impact of feature
selection on the effectiveness of the tag recommendation is also studied. Effectiveness of the proposed
framework is evaluated through precision, recall and f-measure metrics.
Towards Automatic Analysis of Online Discussions among Hong Kong StudentsCITE
HU, Xiao (University of Hong Kong)
http://citers2013.cite.hku.hk/en/paper_619.htm
---------------------------
Author(s) bear(s) the responsibility in case of any infringement of the Intellectual Property Rights of third parties.
---------------------------
CITE was notified by the author(s) that if the presentation slides contain any personal particulars, records and personal data (as defined in the Personal Data (Privacy) Ordinance) such as names, email addresses, photos of students, etc, the author(s) have/has obtained the corresponding person's consent.
Learning Relations from Social Tagging DataHang Dong
An interesting research direction is to discover structured knowledge from user generated data. Our work aims to find relations among social tags and organise them into hierarchies so as to better support discovery and search for online users. We cast relation discovery in this context to a binary classification problem in supervised learning. This approach takes as input features of two tags extracted using probabilistic topic modelling, and predicts whether a broader-narrower relation holds between them. Experiments were conducted using two large, real-world datasets, the Bibsonomy dataset which is used to extract tags and their features, and the DBpedia dataset which is used as the ground truth. Three sets of features were designed and extracted based on topic distri- butions, similarity and probabilistic associations. Evaluation results with respect to the ground truth demonstrate that our method outperforms existing ones based on various features and heuristics. Future studies are suggested to study the Knowledge Base Enrichment from folksonomies and deep neural network approaches to process tagging data.
FACILITATING VIDEO SOCIAL MEDIA SEARCH USING SOCIAL-DRIVEN TAGS COMPUTINGcsandit
Online video search or stream live on social media has become tremendous widespread and
speedy increased continuously in recent years. Most of the videos shared on social media are
aimed at the more number of views from audiences. What and how many videos the users
shared all around the world have created a great amount and varied videos and the other data
into Internet cloud’s database and even can be viewed as a kind of big data of digital contents.
This research is to present how to implement a social-driven tags computing (SDT) which can
be used to facilitate online video search on social media platforms
Learning to Classify Users in Online Interaction NetworksSymeon Papadopoulos
Presentation given at ICCSS 2015, Helsinki, Finland. It illustrates an approach for classifying users of OSNs solely based on their interactions with other users.
Keynote at 4th Annual KnowEscape Conference, Sofia, Bulgaria (Feb 24, 2017). http://knowescape.org/knowescape2017/
Yes, we’re open: Open science & altmetrics
Abstract: Open Science is en vogue – especially after Carlos Moedas, EU-Commissioner for Research, Science and Innovation, has outlined his vision for Europe along the lines of ‘open innovation, open science, open to the world’. Open science describes the transition of ‘publishing as fast as possible’ towards ‘sharing knowledge as fast as possible’. Several reasons explain the move towards openness, it is expected, for example, that open science will increase the efficiency of science. Of course, digital media and web-based environments are keys to this development, but it also requires a systemic change to transform open science from a nice-to-have-feature into the default way of performing research. Altmetrics, i.e. social media-based metrics, are often considered drivers of open science and essential tools for changing the reward system in science. When looking closer, though, severe tensions between features as well as expectations of open science and altmetrics become apparent. The talk will argue that open science only can enfold its potential if ‘openness’ is fully embraced and supported by open metrics.
Impulsstatement barcamp wissenschaft 20_forschung neu denkenIsabella Peters
Impulsstatement zum Barcamp "Wissenschaft 2.0 - Forschung neu denken". 17./18.10.14 Hamburg.
Informationen zur Veranstaltung: http://www.wissenschaft-kontrovers.de/wissenschaft-2-0-forschung-neu-denken/
1. Tags as Tools for Social Classification Dr. Isabella Peters Department of Information Science Institute for Language and Information Heinrich-Heine-University Düsseldorf, Germany 34th Annual Conference of the German Classification Society, July 2010
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18. Conclusion What type of tag distribution ? Tag distribution stable? Extraction of Power Tags I & II Pairs of relevant Power Tags Candidate vocabulary Definition of concepts and of semantic relations Intellectual structuring Social knowledge organization system Automatic processing Intellectual processing
19. Comments? Questions? Isabella Peters: isabella.peters@uni-duesseldorf.de Greetings from Düsseldorf! This presentation is available on SlideShare: http://www.slideshare.net/isabellapeters.