SocialLink establishes a link between DBpedia and Twitter, centered on popular entities occurring in both of them, which enables transferring knowledge from one resource to another and back. On this page, we describe three example use cases where this capability can be leveraged.

DBpedia to Twitter: User Profiling. The task of inferring users attributes based on their digital footprint is typically referred to as user profiling. Prediction of various attributes based on a person's social graph, posted content or other attributes is popular among researchers and companies. However, in most setups, namely supervised machine learning-based ones, user profiling requires significant amounts of manual labour to construct training sets. This both limits the possible attributes that can be inferred and the applicability of approaches operating on large amounts of training data, such as Deep Neural Networks.

Recently, researchers focused on automatic crawling of user profiling datasets from social media. However, even the largest datasets only contain few thousands examples per property and are limited to properties explicitly present in social media.

SocialLink helps social media researchers to tackle user profiling by providing accurate machine-readable descriptions for hundreds of thousands of social media profiles. Any attribute that is present in DBpedia can now be modeled without relying on expensive manual annotation and our resource can be used both to train and to evaluate any proposed attribute classifiers.

Another simple example is the inference of user interests based on a social graph. Imagine a user that follows a set of accounts that have alignments in SocialLink. By using this information, one can try to model interests of this person, his/her location and language just by looking at the DBpedia properties of the accounts that she/he likes. For instance, following dbr:SpaceX and dbr:NASA can point on a dbr:Aerospace_engineering industry fan, while the increased amount of dbr:Donald_Trump-related tweets can reveal a dbr:GOP supporter.

DBpedia to Twitter: Entity Linking. Another use case is the Named Entity Linking (NEL) task, whose goal is to link mentions of named entities in a text to their corresponding entities in a KB such as DBpedia. Challenging on its own, the NEL task presents additional unique challenges when applied to social media posts due to noisiness, lack of sufficient textual context and their informal nature (e.g., use of slang).

It is worth noticing that social media posts typically contain explicit mentions of social media accounts in the form of @username snippets. When referring to Twitter, some of these mentions (especially the ones referring to popular accounts) may appear in SocialLink, and thus can be directly disambiguated to DBpedia with high precision using our resource. Apart being part of the NEL result, these links provide additional contextual information (injected from DBpedia) that can be leveraged for disambiguating other named entities occurring in the post.

SocialLink was used in this capacity by two teams participating to a NEL challenge on Italian tweets (NEEL-IT task) as part of the EVALITA 2016 campaign, allowing both of them to improve their results.

It is worth noting that the two-step approach of the SocialLink pipeline can be adapted to directly disambiguate named entities in regular texts against the social media. Such functionality is present in the Social Media Toolkit that is among the complementary tools available on the SocialLink website.

Twitter to DBpedia: Extracting FOAF Profiles. Up-to-date information about DBpedia persons and organisations can be extracted from Twitter after an alignment is established through SocialLink. Focusing on persons, different user profile properties expressible with FOAF may be extracted from a DBpedia person's Twitter account. They include:

• Basic properties like foaf:name, foaf:surname, foaf:gender, foaf:birthday, and foaf:depiction linking to user images rather scarce in Wikipedia/DBpedia, often available in Twitter user descriptions and profile metadata;
• Acquaintances (foaf:knows), extracted from friends, followers and Twitter accounts a user interacted with that are aligned to DBpedia entities in SocialLink
• Links to homepages (foaf:homepage and similar) and other web resources from a Twitter user description and posts, that can be matched to external links in DBpedia to mine relations with other DBpedia entities (e.g., affiliation, authorship, participation, all expressible in FOAF), which may be possibly disambiguated using natural language understanding techniques.

While a basic FOAF profile can be extracted from any Twitter account, the links to DBpedia provided by SocialLink allow grounding the extracted data and disambiguating the values of object properties with respect to a larger knowledge base, this way increasing the usefulness of extracted FOAF profiles.

To read

  PRAI 2018 paper

  ISWC 2017 paper

  SAC 2017 paper

  ISWC 2017 talk

To cite

                    
@article{sociallink2018prai,
  author    = {Yaroslav Nechaev and
               Francesco Corcoglioniti and
               Claudio Giuliano},
  title     = {SocialLink: exploiting graph embeddings to link DBpedia entities to
               Twitter profiles},
  journal   = {Progress in {AI}},
  volume    = {7},
  number    = {4},
  pages     = {251--272},
  year      = {2018}
}

                

Canonical citations (DOIs) for the dataset are available via Zenodo Zenodo (all versions) and Springer Nature (version v2.0) digital repositories (see DOI links in the footer).