2,239 research outputs found
TotalDefMeme: A Multi-Attribute Meme dataset on Total Defence in Singapore
Total Defence is a defence policy combining and extending the concept of
military defence and civil defence. While several countries have adopted total
defence as their defence policy, very few studies have investigated its
effectiveness. With the rapid proliferation of social media and digitalisation,
many social studies have been focused on investigating policy effectiveness
through specially curated surveys and questionnaires either through digital
media or traditional forms. However, such references may not truly reflect the
underlying sentiments about the target policies or initiatives of interest.
People are more likely to express their sentiment using communication mediums
such as starting topic thread on forums or sharing memes on social media. Using
Singapore as a case reference, this study aims to address this research gap by
proposing TotalDefMeme, a large-scale multi-modal and multi-attribute meme
dataset that captures public sentiments toward Singapore's Total Defence
policy. Besides supporting social informatics and public policy analysis of the
Total Defence policy, TotalDefMeme can also support many downstream multi-modal
machine learning tasks, such as aspect-based stance classification and
multi-modal meme clustering. We perform baseline machine learning experiments
on TotalDefMeme and evaluate its technical validity, and present possible
future interdisciplinary research directions and application scenarios using
the dataset as a baseline.Comment: 6 pages. Accepted at ACM MMSys 202
On Explaining Multimodal Hateful Meme Detection Models
Hateful meme detection is a new multimodal task that has gained significant
traction in academic and industry research communities. Recently, researchers
have applied pre-trained visual-linguistic models to perform the multimodal
classification task, and some of these solutions have yielded promising
results. However, what these visual-linguistic models learn for the hateful
meme classification task remains unclear. For instance, it is unclear if these
models are able to capture the derogatory or slurs references in multimodality
(i.e., image and text) of the hateful memes. To fill this research gap, this
paper propose three research questions to improve our understanding of these
visual-linguistic models performing the hateful meme classification task. We
found that the image modality contributes more to the hateful meme
classification task, and the visual-linguistic models are able to perform
visual-text slurs grounding to a certain extent. Our error analysis also shows
that the visual-linguistic models have acquired biases, which resulted in
false-positive predictions
SBTRec- A Transformer Framework for Personalized Tour Recommendation Problem with Sentiment Analysis
When traveling to an unfamiliar city for holidays, tourists often rely on
guidebooks, travel websites, or recommendation systems to plan their daily
itineraries and explore popular points of interest (POIs). However, these
approaches may lack optimization in terms of time feasibility, localities, and
user preferences. In this paper, we propose the SBTRec algorithm: a BERT-based
Trajectory Recommendation with sentiment analysis, for recommending
personalized sequences of POIs as itineraries. The key contributions of this
work include analyzing users' check-ins and uploaded photos to understand the
relationship between POI visits and distance. We introduce SBTRec, which
encompasses sentiment analysis to improve recommendation accuracy by
understanding users' preferences and satisfaction levels from reviews and
comments about different POIs. Our proposed algorithms are evaluated against
other sequence prediction methods using datasets from 8 cities. The results
demonstrate that SBTRec achieves an average F1 score of 61.45%, outperforming
baseline algorithms.
The paper further discusses the flexibility of the SBTRec algorithm, its
ability to adapt to different scenarios and cities without modification, and
its potential for extension by incorporating additional information for more
reliable predictions. Overall, SBTRec provides personalized and relevant POI
recommendations, enhancing tourists' overall trip experiences. Future work
includes fine-tuning personalized embeddings for users, with evaluation of
users' comments on POIs,~to further enhance prediction accuracy
Decoding the Underlying Meaning of Multimodal Hateful Memes
Recent studies have proposed models that yielded promising performance for
the hateful meme classification task. Nevertheless, these proposed models do
not generate interpretable explanations that uncover the underlying meaning and
support the classification output. A major reason for the lack of explainable
hateful meme methods is the absence of a hateful meme dataset that contains
ground truth explanations for benchmarking or training. Intuitively, having
such explanations can educate and assist content moderators in interpreting and
removing flagged hateful memes. This paper address this research gap by
introducing Hateful meme with Reasons Dataset (HatReD), which is a new
multimodal hateful meme dataset annotated with the underlying hateful
contextual reasons. We also define a new conditional generation task that aims
to automatically generate underlying reasons to explain hateful memes and
establish the baseline performance of state-of-the-art pre-trained language
models on this task. We further demonstrate the usefulness of HatReD by
analyzing the challenges of the new conditional generation task in explaining
memes in seen and unseen domains. The dataset and benchmark models are made
available here: https://github.com/Social-AI-Studio/HatRedComment: 9 pages. Accepted by IJCAI 202
BTRec: BERT-Based Trajectory Recommendation for Personalized Tours
An essential task for tourists having a pleasant holiday is to have a
well-planned itinerary with relevant recommendations, especially when visiting
unfamiliar cities. Many tour recommendation tools only take into account a
limited number of factors, such as popular Points of Interest (POIs) and
routing constraints. Consequently, the solutions they provide may not always
align with the individual users of the system. We propose an iterative
algorithm in this paper, namely: BTREC (BERT-based Trajectory Recommendation),
that extends from the POIBERT embedding algorithm to recommend personalized
itineraries on POIs using the BERT framework. Our BTREC algorithm incorporates
users' demographic information alongside past POI visits into a modified BERT
language model to recommend a personalized POI itinerary prediction given a
pair of source and destination POIs. Our recommendation system can create a
travel itinerary that maximizes POIs visited, while also taking into account
user preferences for categories of POIs and time availability. Our
recommendation algorithm is largely inspired by the problem of sentence
completion in natural language processing (NLP). Using a dataset of eight
cities of different sizes, our experimental results demonstrate that our
proposed algorithm is stable and outperforms many other sequence prediction
algorithms, measured by recall, precision, and F1-scores.Comment: RecSys 2023, Workshop on Recommenders in Touris
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