2,328 research outputs found
Association and Temporality between News and Tweets
With the advent of social media, the boundaries of mainstream journalism and social networks are becoming blurred. User-generated content is increasing, and hence, journalists dedicate considerable time searching platforms such as Facebook and Twitter to announce, spread, and monitor news and crowd check information. Many studies have looked at social networks as news sources, but the relationship and interconnections between this type of platform and news media have not been thoroughly investigated. In this work, we have studied a series of news articles and examined a set of related comments on a social network during a period of six months. Specifically, a sample of articles from generalist Portuguese news sources published in the first semester of 2016 was clustered, and the resulting clusters were then associated with tweets of Portuguese users with the recourse to a similarity measure. Focusing on a subset of clusters, we have performed a temporal analysis by examining the evolution of the two types of documents (articles and tweets) and the timing of when they appeared. It appears that for some stories, namely Brexit and the European Football Cup, the publishing of news articles intensifies on key dates (event-oriented), while the discussion on social media is more balanced throughout the months leading up to those events.info:eu-repo/semantics/publishedVersio
Detecting and Explaining Causes From Text For a Time Series Event
Explaining underlying causes or effects about events is a challenging but
valuable task. We define a novel problem of generating explanations of a time
series event by (1) searching cause and effect relationships of the time series
with textual data and (2) constructing a connecting chain between them to
generate an explanation. To detect causal features from text, we propose a
novel method based on the Granger causality of time series between features
extracted from text such as N-grams, topics, sentiments, and their composition.
The generation of the sequence of causal entities requires a commonsense
causative knowledge base with efficient reasoning. To ensure good
interpretability and appropriate lexical usage we combine symbolic and neural
representations, using a neural reasoning algorithm trained on commonsense
causal tuples to predict the next cause step. Our quantitative and human
analysis show empirical evidence that our method successfully extracts
meaningful causality relationships between time series with textual features
and generates appropriate explanation between them.Comment: Accepted at EMNLP 201
Temporal word embeddings for dynamic user profiling in Twitter
The research described in this paper focused on exploring
the domain of user profiling, a nascent and contentious technology which
has been steadily attracting increased interest from the research community as its potential for providing personalised digital services is realised.
An extensive review of related literature revealed that limited research
has been conducted into how temporal aspects of users can be captured
using user profiling techniques. This, coupled with the notable lack of
research into the use of word embedding techniques to capture temporal
variances in language, revealed an opportunity to extend the Random Indexing word embedding technique such that the interests of users could
be modelled based on their use of language. To achieve this, this work
concerned itself with extending an existing implementation of Temporal
Random Indexing to model Twitter users across multiple granularities of
time based on their use of language. The product of this is a novel technique for temporal user profiling, where a set of vectors is used to describe
the evolution of a Twitter user’s interests over time through their use of
language. The vectors produced were evaluated against a temporal implementation of another state-of-the-art word embedding technique, the
Word2Vec Dynamic Independent Skip-gram model, where it was found
that Temporal Random Indexing outperformed Word2Vec in the generation of temporal user profiles
Retweeting my feelings? Exploring the temporal effects of the COVID-19 pandemic on social media use
Social media platform (SMP) use has intensified during the COVID-19 pandemic. New user groups are utilising SMPs more frequently to satisfy their unmet psychological needs. However, research to date has insufficiently explored variations in SMP use due to the pandemic. As the pandemic has adversely impacted the general public’s mental health, we propose and then apply the Temporal-Needs-Affordances-Features (T-NAF) model in this context. Public engagement with two international mental health awareness campaigns on Twitter were tracked over four years. Results show that the pandemic initially coincided with a significant increase in engagement (e.g., retweets) and a significant decrease in network size (e.g., followers). This establishes that a larger proportion of individuals engaged with resharing behaviour as the pandemic commenced, reinforcing the importance of SMPs in relation to mental health and needs satisfaction. This study also highlights the importance of temporality in social media research. Future research pathways are discussed
Modeling Temporal Evidence from External Collections
Newsworthy events are broadcast through multiple mediums and prompt the
crowds to produce comments on social media. In this paper, we propose to
leverage on this behavioral dynamics to estimate the most relevant time periods
for an event (i.e., query). Recent advances have shown how to improve the
estimation of the temporal relevance of such topics. In this approach, we build
on two major novelties. First, we mine temporal evidences from hundreds of
external sources into topic-based external collections to improve the
robustness of the detection of relevant time periods. Second, we propose a
formal retrieval model that generalizes the use of the temporal dimension
across different aspects of the retrieval process. In particular, we show that
temporal evidence of external collections can be used to (i) infer a topic's
temporal relevance, (ii) select the query expansion terms, and (iii) re-rank
the final results for improved precision. Experiments with TREC Microblog
collections show that the proposed time-aware retrieval model makes an
effective and extensive use of the temporal dimension to improve search results
over the most recent temporal models. Interestingly, we observe a strong
correlation between precision and the temporal distribution of retrieved and
relevant documents.Comment: To appear in WSDM 201
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Storying leaks for sharing: The case of leaking the “Moscovici draft” on Twitter
This article proposes a discourse-narrative approach to news making online as a networked practice of storying and sharing. This approach is illustrated in the examination of the release of a draft Eurogroup statement via journalist Paul Mason’s Facebook, Scribd and Twitter accounts on the 16th February 2015. The analysis draws on small story insights (Georgakopoulou, 2015) and the empirical framework of sharing (Androutsopoulos, 2014). It shows how the release of this leak event on Twitter is storied as a breaking news story unfolding moment-by-moment as it happens, at the same time as making up an incipient record of the event as it happened. It is argued that breaking news (micro)stories are shared as moments of narrative stancetaking, featuring a concise, portable storyline and cumulative evaluation(s) that foreground the relevance of the leak for the ongoing discussions on the Greek bailout negotiations as well as the continued importance of the journalist as the mediator of the leak. In this case of sharing a leaked document with networked participants, narrativity is drawn upon as a key resource for producing and circulating alternative stances on the Greek crisis, creating a range of networked participation positions. This article contributes to the study of news sharing online and digital storytelling based on the qualitative analysis of ‘small’ data
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