1,844 research outputs found
Exploring Image Virality in Google Plus
Reactions to posts in an online social network show different dynamics
depending on several textual features of the corresponding content. Do similar
dynamics exist when images are posted? Exploiting a novel dataset of posts,
gathered from the most popular Google+ users, we try to give an answer to such
a question. We describe several virality phenomena that emerge when taking into
account visual characteristics of images (such as orientation, mean saturation,
etc.). We also provide hypotheses and potential explanations for the dynamics
behind them, and include cases for which common-sense expectations do not hold
true in our experiments.Comment: 8 pages, 8 figures. IEEE/ASE SocialCom 201
Deep Feelings: A Massive Cross-Lingual Study on the Relation between Emotions and Virality
ABSTRACT This article provides a comprehensive investigation on the relations between virality of news articles and the emotions they are found to evoke. Virality, in our view, is a phenomenon with many facets, i.e. under this generic term several different effects of persuasive communication are comprised. By exploiting a high-coverage and bilingual corpus of documents containing metrics of their spread on social networks as well as a massive affective annotation provided by readers, we present a thorough analysis of the interplay between evoked emotions and viral facets. We highlight and discuss our findings in light of a cross-lingual approach: while we discover differences in evoked emotions and corresponding viral effects, we provide preliminary evidence of a generalized explanatory model rooted in the deep structure of emotions: the Valence-Arousal-Dominance (VAD) circumplex. We find that viral facets appear to be consistently affected by particular VAD configurations, and these configurations indicate a clear connection with distinct phenomena underlying persuasive communication
Toward Stance-based Personas for Opinionated Dialogues
In the context of chit-chat dialogues it has been shown that endowing systems
with a persona profile is important to produce more coherent and meaningful
conversations. Still, the representation of such personas has thus far been
limited to a fact-based representation (e.g. "I have two cats."). We argue that
these representations remain superficial w.r.t. the complexity of human
personality. In this work, we propose to make a step forward and investigate
stance-based persona, trying to grasp more profound characteristics, such as
opinions, values, and beliefs to drive language generation. To this end, we
introduce a novel dataset allowing to explore different stance-based persona
representations and their impact on claim generation, showing that they are
able to grasp abstract and profound aspects of the author persona.Comment: Accepted at Findings of EMNLP 202
SALSA: A Novel Dataset for Multimodal Group Behavior Analysis
Studying free-standing conversational groups (FCGs) in unstructured social
settings (e.g., cocktail party ) is gratifying due to the wealth of information
available at the group (mining social networks) and individual (recognizing
native behavioral and personality traits) levels. However, analyzing social
scenes involving FCGs is also highly challenging due to the difficulty in
extracting behavioral cues such as target locations, their speaking activity
and head/body pose due to crowdedness and presence of extreme occlusions. To
this end, we propose SALSA, a novel dataset facilitating multimodal and
Synergetic sociAL Scene Analysis, and make two main contributions to research
on automated social interaction analysis: (1) SALSA records social interactions
among 18 participants in a natural, indoor environment for over 60 minutes,
under the poster presentation and cocktail party contexts presenting
difficulties in the form of low-resolution images, lighting variations,
numerous occlusions, reverberations and interfering sound sources; (2) To
alleviate these problems we facilitate multimodal analysis by recording the
social interplay using four static surveillance cameras and sociometric badges
worn by each participant, comprising the microphone, accelerometer, bluetooth
and infrared sensors. In addition to raw data, we also provide annotations
concerning individuals' personality as well as their position, head, body
orientation and F-formation information over the entire event duration. Through
extensive experiments with state-of-the-art approaches, we show (a) the
limitations of current methods and (b) how the recorded multiple cues
synergetically aid automatic analysis of social interactions. SALSA is
available at http://tev.fbk.eu/salsa.Comment: 14 pages, 11 figure
Countering Misinformation via Emotional Response Generation
The proliferation of misinformation on social media platforms (SMPs) poses a
significant danger to public health, social cohesion and ultimately democracy.
Previous research has shown how social correction can be an effective way to
curb misinformation, by engaging directly in a constructive dialogue with users
who spread -- often in good faith -- misleading messages. Although professional
fact-checkers are crucial to debunking viral claims, they usually do not engage
in conversations on social media. Thereby, significant effort has been made to
automate the use of fact-checker material in social correction; however, no
previous work has tried to integrate it with the style and pragmatics that are
commonly employed in social media communication. To fill this gap, we present
VerMouth, the first large-scale dataset comprising roughly 12 thousand
claim-response pairs (linked to debunking articles), accounting for both
SMP-style and basic emotions, two factors which have a significant role in
misinformation credibility and spreading. To collect this dataset we used a
technique based on an author-reviewer pipeline, which efficiently combines LLMs
and human annotators to obtain high-quality data. We also provide comprehensive
experiments showing how models trained on our proposed dataset have significant
improvements in terms of output quality and generalization capabilities.Comment: Accepted to EMNLP 2023 main conferenc
ColdGANs: Taming Language GANs with Cautious Sampling Strategies
Training regimes based on Maximum Likelihood Estimation (MLE) suffer from
known limitations, often leading to poorly generated text sequences. At the
root of these limitations is the mismatch between training and inference, i.e.
the so-called exposure bias, exacerbated by considering only the reference
texts as correct, while in practice several alternative formulations could be
as good. Generative Adversarial Networks (GANs) can mitigate those limitations
but the discrete nature of text has hindered their application to language
generation: the approaches proposed so far, based on Reinforcement Learning,
have been shown to underperform MLE. Departing from previous works, we analyze
the exploration step in GANs applied to text generation, and show how classical
sampling results in unstable training. We propose to consider alternative
exploration strategies in a GAN framework that we name ColdGANs, where we force
the sampling to be close to the distribution modes to get smoother learning
dynamics. For the first time, to the best of our knowledge, the proposed
language GANs compare favorably to MLE, and obtain improvements over the
state-of-the-art on three generative tasks, namely unconditional text
generation, question generation, and abstractive summarization
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