12 research outputs found
TAG technical manual
Transient electric network analyzer computer program manua
A customisable pipeline for continuously harvesting socially-minded Twitter users
On social media platforms and Twitter in particular, specific classes of
users such as influencers have been given satisfactory operational definitions
in terms of network and content metrics.
Others, for instance online activists, are not less important but their
characterisation still requires experimenting.
We make the hypothesis that such interesting users can be found within
temporally and spatially localised contexts, i.e., small but topical fragments
of the network containing interactions about social events or campaigns with a
significant footprint on Twitter.
To explore this hypothesis, we have designed a continuous user profile
discovery pipeline that produces an ever-growing dataset of user profiles by
harvesting and analysing contexts from the Twitter stream.
The profiles dataset includes key network and content-based users metrics,
enabling experimentation with user-defined score functions that characterise
specific classes of online users.
The paper describes the design and implementation of the pipeline and its
empirical evaluation on a case study consisting of healthcare-related campaigns
in the UK, showing how it supports the operational definitions of online
activism, by comparing three experimental ranking functions. The code is
publicly available.Comment: Procs. ICWE 2019, June 2019, Kore
Assessing Sentiment of the Expressed Stance on Social Media
Stance detection is the task of inferring viewpoint towards a given topic or
entity either being supportive or opposing. One may express a viewpoint towards
a topic by using positive or negative language. This paper examines how the
stance is being expressed in social media according to the sentiment polarity.
There has been a noticeable misconception of the similarity between the stance
and sentiment when it comes to viewpoint discovery, where negative sentiment is
assumed to mean against stance, and positive sentiment means in-favour stance.
To analyze the relation between stance and sentiment, we construct a new
dataset with four topics and examine how people express their viewpoint with
regards these topics. We validate our results by carrying a further analysis of
the popular stance benchmark SemEval stance dataset. Our analyses reveal that
sentiment and stance are not highly aligned, and hence the simple sentiment
polarity cannot be used solely to denote a stance toward a given topic.Comment: Accepted as a full paper at Socinfo 2019. Please cite the Socinfo
versio