1,720,966 research outputs found
SocialLink: exploiting graph embeddings to link DBpedia entities to Twitter profiles
Full text linkSocialLink is a project designed to match social media profiles on Twitter to corresponding entities in DBpedia. Built to bridge the vibrant Twitter social media world and the Linked Open Data cloud, SocialLink enables knowledge transfer between the two, both assisting Semantic Web practitioners in better harvesting the vast amounts of information available on Twitter and allowing leveraging of DBpedia data for social media analysis tasks. In this paper, we further extend the original SocialLink approach by exploiting graph-based features based on both DBpedia and Twitter, represented as graph embeddings learned from vast amounts of unlabeled data. The introduction of such new features required to redesign our deep neural network-based candidate selection algorithm and, as a result, we experimentally demonstrate a significant improvement of the performances of SocialLink
Concealing interests of passive users in social media
No full textUser profiling has existed in the social media since their inception and has supported most of their business model. Even if users do not actively share the information about themselves on the social media (so-called passive users), they can still be profiled based on their location and who they follow. In this paper, we present a system that leverages the linking of followed (popular) Twitter users to DBpedia, and the information therein contained, to help users concealing their digital footprint. Specifically, our approach helps a passive Twitter user to stay private by proposing a list of additional profiles to follow that would confuse the social media’s inference pipeline and prevent it from inferring useful information about that passive user and his interests
MicroNeel: Combining NLP Tools to Perform Named Entity Detection and Linking on Microposts
No full textIn this paper we present the Mi- croNeel system for Named Entity Recognition and Entity Linking on Italian microposts, which participated in the NEEL-IT task at EVALITA 2016. MicroNeel combines The Wiki Machine and Tint, two standard NLP tools, with comprehensive tweet preprocessing, the Twitter-DBpedia alignments from the Social Media Toolkit resource, and rule-based or supervised merging of produced annotations
Twitter User Recommendation for Gaining Followers
No full textWhile social media presence is increasingly important for businesses, growing a social media account and improving its reputation by gathering followers are time-consuming tasks, especially for professionals and small businesses lacking the necessary skills and resources. With the broader goal of providing automatic tool support for social media account automation, in this paper we consider the problem of recommending a Twitter account manager a top-K list of Twitter users that, if approached - e.g., followed, mentioned, or otherwise targeted on social media - are likely to follow the account and interact with it, this way improving its reputation. We propose a recommendation system tackling this problem that leverages features ranging from basic social media attributes to specialized, domain-relevant user profile attributes predicted from data using machine learning techniques, and we report on a preliminary analysis of its performance in gathering new followers in a Twitter scenario where the account manager follows recommended users to trigger their follow-back
SocialLink: Linking DBpedia entities to corresponding Twitter accounts
No full textWe present SocialLink, a publicly available Linked Open Data dataset that matches social media accounts on Twitter to the corresponding entities in multiple language chapters of DBpedia. By effectively bridging the Twitter social media world and the Linked Open Data cloud, SocialLink enables knowledge transfer between the two: on the one hand, it supports Semantic Web practitioners in better harvesting the vast amounts of valuable, up-to-date information available in Twitter; on the other hand, it permits Social Media researchers to leverage DBpedia data when processing the noisy, semi-structured data of Twitter. SocialLink is automatically updated with periodic releases and the code along with the gold standard dataset used for its training are made available as an open source project
Linking knowledge bases to social media profiles
No full textSocial media have become an invaluable source of data for a wide variety of tasks. Unfortunately, this data is hard to gather and process due to low amount of machine readable attributes, API limitations and noisiness. In this paper we propose a system that aligns knowledge base entries of people and organisations to the corresponding social media profiles. The motivation is twofold:(i) on the one hand, we facilitate processing of social media data by allowing the import of rich entity descriptions from knowledge bases;(ii) on the other hand, we are enabling an automatic enrichment of a knowledge base with additional data from the social media. We used this system to create a resource of 893,446 alignments between DBpedia entities and Twitter profiles. This resource allows, effectively, to connect Twitter to the Linked Open Data cloud
Type Prediction Combining Linked Open Data and Social Media
No full textLinked Open Data (LOD) and social media often contain the representations of the same real-world entities, such as persons and organizations. These representations are increasingly interlinked, making it possible to combine and leverage both LOD and social media data in prediction problems, complementing their relative strengths: while LOD knowledge is highly structured but also scarce and obsolete for some entities, social media data provide real-time updates and increased coverage, albeit being mostly unstructured. In this paper, we investigate the feasibility of using social media data to perform type prediction for entities in a LOD knowledge graph. We discuss how to gather training data for such a task, and how to build an efficient domain-independent vector representation of entities based on social media data. Our experiments on several type prediction tasks using DBpedia and Twitter data show the effectiveness of this representation, both alone and combined with knowledge graph-based features, suggesting its potential for ontology population
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Pokedem: An automatic social media management application
No full textTypically, the task of managing the social media presence of a company or a public person is the job of a dedicated social media account manager. While many attempts have been made in recent years to provide more automation to account managers, complete workflow automation has still to be achieved. Pokedem is a social media management application that aims at filling this gap, recommending actions that could be performed by the account manager to increase the popularity of a Twitter account and the engagement of its audience. By casting the problem in the setting of recommendation systems, Pokedem is able to provide account managers with a complete tool for automating their daily activities
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