5 research outputs found
Leveraging Label Correlations in a Multi-label Setting: A Case Study in Emotion
Detecting emotions expressed in text has become critical to a range of
fields. In this work, we investigate ways to exploit label correlations in
multi-label emotion recognition models to improve emotion detection. First, we
develop two modeling approaches to the problem in order to capture word
associations of the emotion words themselves, by either including the emotions
in the input, or by leveraging Masked Language Modeling (MLM). Second, we
integrate pairwise constraints of emotion representations as regularization
terms alongside the classification loss of the models. We split these terms
into two categories, local and global. The former dynamically change based on
the gold labels, while the latter remain static during training. We demonstrate
state-of-the-art performance across Spanish, English, and Arabic in SemEval
2018 Task 1 E-c using monolingual BERT-based models. On top of better
performance, we also demonstrate improved robustness. Code is available at
https://github.com/gchochla/Demux-MEmo.Comment: Accepted at ICASSP'23, 5 pages, 1 figur
Using Emotion Embeddings to Transfer Knowledge Between Emotions, Languages, and Annotation Formats
The need for emotional inference from text continues to diversify as more and
more disciplines integrate emotions into their theories and applications. These
needs include inferring different emotion types, handling multiple languages,
and different annotation formats. A shared model between different
configurations would enable the sharing of knowledge and a decrease in training
costs, and would simplify the process of deploying emotion recognition models
in novel environments. In this work, we study how we can build a single model
that can transition between these different configurations by leveraging
multilingual models and Demux, a transformer-based model whose input includes
the emotions of interest, enabling us to dynamically change the emotions
predicted by the model. Demux also produces emotion embeddings, and performing
operations on them allows us to transition to clusters of emotions by pooling
the embeddings of each cluster. We show that Demux can simultaneously transfer
knowledge in a zero-shot manner to a new language, to a novel annotation format
and to unseen emotions. Code is available at
https://github.com/gchochla/Demux-MEmo .Comment: Accepted at ICASSP'23, 5 page
Socio-Linguistic Characteristics of Coordinated Inauthentic Accounts
Online manipulation is a pressing concern for democracies, but the actions
and strategies of coordinated inauthentic accounts, which have been used to
interfere in elections, are not well understood. We analyze a five
million-tweet multilingual dataset related to the 2017 French presidential
election, when a major information campaign led by Russia called "#MacronLeaks"
took place. We utilize heuristics to identify coordinated inauthentic accounts
and detect attitudes, concerns and emotions within their tweets, collectively
known as socio-linguistic characteristics. We find that coordinated accounts
retweet other coordinated accounts far more than expected by chance, while
being exceptionally active just before the second round of voting.
Concurrently, socio-linguistic characteristics reveal that coordinated accounts
share tweets promoting a candidate at three times the rate of non-coordinated
accounts. Coordinated account tactics also varied in time to reflect news
events and rounds of voting. Our analysis highlights the utility of
socio-linguistic characteristics to inform researchers about tactics of
coordinated accounts and how these may feed into online social manipulation.Comment: 12 pages, 9 figure
Representation of professions in entertainment media: Insights into frequency and sentiment trends through computational text analysis.
Societal ideas and trends dictate media narratives and cinematic depictions which in turn influence people's beliefs and perceptions of the real world. Media portrayal of individuals and social institutions related to culture, education, government, religion, and family affect their function and evolution over time as people perceive and incorporate the representations from portrayals into their everyday lives. It is important to study media depictions of social structures so that they do not propagate or reinforce negative stereotypes, or discriminate against a particular section of the society. In this work, we examine media representation of different professions and provide computational insights into their incidence, and sentiment expressed, in entertainment media content. We create a searchable taxonomy of professional groups, synsets, and titles to facilitate their retrieval from short-context speaker-agnostic text passages like movie and television (TV) show subtitles. We leverage this taxonomy and relevant natural language processing models to create a corpus of professional mentions in media content, spanning more than 136,000 IMDb titles over seven decades (1950-2017). We analyze the frequency and sentiment trends of different occupations, study the effect of media attributes such as genre, country of production, and title type on these trends, and investigate whether the incidence of professions in media subtitles correlate with their real-world employment statistics. We observe increased media mentions over time of STEM, arts, sports, and entertainment occupations in the analyzed subtitles, and a decreased frequency of manual labor jobs and military occupations. The sentiment expressed toward lawyers, police, and doctors showed increasing negative trends over time, whereas the mentions about astronauts, musicians, singers, and engineers appear more favorably. We found that genre is a good predictor of the type of professions mentioned in movies and TV shows. Professions that employ more people showed increased media frequency