30,281 research outputs found
Fine-grained Emotion Role Detection Based on Retweet Information
User behaviors in online social networks convey not only literal information but also one’s emotion attitudes towards the information. To compute this attitude, we define the concept of emotion role as the concentrated reflection of a user’s online emotional characteristics. Emotion role detection aims to better understand the structure and sentiments of online social networks and support further analysis, e.g., revealing public opinions, providing personalized recommendations, and detecting influential users. In this paper, we first introduce the definition of a fine-grained emotion role, which consists of two dimensions: emotion orientation (i.e., positive, negative, and neutral) and emotion influence (i.e., leader and follower). We then propose a Multi-dimensional Emotion Role Mining model, named as MERM, to determine a user’s emotion role in online social networks.
Specifically, we tend to identify emotion roles by combining a set of features that reflect a user’s online emotional status, including degree of emotional characteristics, accumulated emotion preference, structural factor, temporal factor and emotion change factor. Experiment results on a real-life micro-blog reposting dataset show that the classification accuracy of the proposed model can achieve up to 90.1%
Linguistic Markers of Influence in Informal Interactions
There has been a long standing interest in understanding `Social Influence'
both in Social Sciences and in Computational Linguistics. In this paper, we
present a novel approach to study and measure interpersonal influence in daily
interactions. Motivated by the basic principles of influence, we attempt to
identify indicative linguistic features of the posts in an online knitting
community. We present the scheme used to operationalize and label the posts
with indicator features. Experiments with the identified features show an
improvement in the classification accuracy of influence by 3.15%. Our results
illustrate the important correlation between the characteristics of the
language and its potential to influence others.Comment: 10 pages, Accepted in NLP+CSS workshop for ACL (Association for
Computational Linguistics) 201
Hierarchical Propagation Networks for Fake News Detection: Investigation and Exploitation
Consuming news from social media is becoming increasingly popular. However,
social media also enables the widespread of fake news. Because of its
detrimental effects brought by social media, fake news detection has attracted
increasing attention. However, the performance of detecting fake news only from
news content is generally limited as fake news pieces are written to mimic true
news. In the real world, news pieces spread through propagation networks on
social media. The news propagation networks usually involve multi-levels. In
this paper, we study the challenging problem of investigating and exploiting
news hierarchical propagation network on social media for fake news detection.
In an attempt to understand the correlations between news propagation
networks and fake news, first, we build a hierarchical propagation network from
macro-level and micro-level of fake news and true news; second, we perform a
comparative analysis of the propagation network features of linguistic,
structural and temporal perspectives between fake and real news, which
demonstrates the potential of utilizing these features to detect fake news;
third, we show the effectiveness of these propagation network features for fake
news detection. We further validate the effectiveness of these features from
feature important analysis. Altogether, this work presents a data-driven view
of hierarchical propagation network and fake news and paves the way towards a
healthier online news ecosystem.Comment: 10 page
Seed selection for information cascade in multilayer networks
Information spreading is an interesting field in the domain of online social
media. In this work, we are investigating how well different seed selection
strategies affect the spreading processes simulated using independent cascade
model on eighteen multilayer social networks. Fifteen networks are built based
on the user interaction data extracted from Facebook public pages and tree of
them are multilayer networks downloaded from public repository (two of them
being Twitter networks). The results indicate that various state of the art
seed selection strategies for single-layer networks like K-Shell or VoteRank do
not perform so well on multilayer networks and are outperformed by Degree
Centrality
Detecting Real-World Influence Through Twitter
In this paper, we investigate the issue of detecting the real-life influence
of people based on their Twitter account. We propose an overview of common
Twitter features used to characterize such accounts and their activity, and
show that these are inefficient in this context. In particular, retweets and
followers numbers, and Klout score are not relevant to our analysis. We thus
propose several Machine Learning approaches based on Natural Language
Processing and Social Network Analysis to label Twitter users as Influencers or
not. We also rank them according to a predicted influence level. Our proposals
are evaluated over the CLEF RepLab 2014 dataset, and outmatch state-of-the-art
ranking methods.Comment: 2nd European Network Intelligence Conference (ENIC), Sep 2015,
Karlskrona, Swede
Bots increase exposure to negative and inflammatory content in online social systems
Societies are complex systems which tend to polarize into sub-groups of
individuals with dramatically opposite perspectives. This phenomenon is
reflected -- and often amplified -- in online social networks where, however,
humans are no more the only players, and co-exist alongside with social bots,
i.e., software-controlled accounts. Analyzing large-scale social data collected
during the Catalan referendum for independence on October 1, 2017, consisting
of nearly 4 millions Twitter posts generated by almost 1 million users, we
identify the two polarized groups of Independentists and Constitutionalists and
quantify the structural and emotional roles played by social bots. We show that
bots act from peripheral areas of the social system to target influential
humans of both groups, bombarding Independentists with violent contents,
increasing their exposure to negative and inflammatory narratives and
exacerbating social conflict online. Our findings stress the importance of
developing countermeasures to unmask these forms of automated social
manipulation.Comment: 8 pages, 5 figure
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