Detecting Inspiring Content on Social Media

Ignat, Oana; Boureau, Y-Lan; Yu, Jane A.; Halevy, Alon

doi:10.1109/acii52823.2021.9597431

Cited by 6 publications

(2 citation statements)

References 42 publications

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“…With the increase in popularity of social media websites (e.g. Facebook, Reddit, Twitter), NLP researchers started using the textual data collected from these platforms for detecting emotions [4,5,6], offensive content [7,8,9], hate speech [10,11], humour [12,13,14], sarcasm [15,16,17], pejorative language [18], inspirational content [19], optimism [20,21] and the manifestations of mental health problems such as depression [22,23], suicide ideation [24,25] and anxiety [26]. Researchers explored the online content from social media even further and began focusing on the multi-modal data [27,28], including internet memes.…”

Section: Related Workmentioning

confidence: 99%

BLUE at Memotion 2.0 2022: You have my Image, my Text and my Transformer

Bucur¹,

Cosma²,

Ioan-Bogdan³

2022

Preprint

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Memes are prevalent on the internet and continue to grow and evolve alongside our culture. An automatic understanding of memes propagating on the internet can shed light on the general sentiment and cultural attitudes of people. In this work, we present team BLUE's solution for the second edition of the MEMOTION shared task. We showcase two approaches for meme classification (i.e. sentiment, humour, offensive, sarcasm and motivation levels) using a text-only method using BERT, and a Multi-Modal-Multi-Task transformer network that operates on both the meme image and its caption to output the final scores. In both approaches, we leverage state-of-the-art pretrained models for text (BERT, Sentence Transformer) and image processing (EfficientNetV4, CLIP). Through our efforts, we obtain first place in task A, second place in task B and third place in task C. In addition, our team obtained the highest average score for all three tasks.

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Section: Related Workmentioning

confidence: 99%

BLUE at Memotion 2.0 2022: You have my Image, my Text and my Transformer

Bucur¹,

Cosma²,

Ioan-Bogdan³

2022

Preprint

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“…The Flickr LDL dataset [58], for example, contains images labeled according to a taxonomy that uses Ekman's six but also includes amusement and contentment, akin to entertained and relaxed in our current work. We also know from prior work that adequately detecting inspirational [16] and informative [28,32] content as well as content expressing gratitude [42] is beneficial for users. Craig et al [5] also found that the primary reasons adolescents use social media is because they want to be entertained, be informed, and feel connected to others.…”

Section: Affective Taxonomiesmentioning

confidence: 99%

Affective Signals in a Social Media Recommender System

Dwivedi-Yu,

Wang,

Qin

et al. 2022

Preprint

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People come to social media to satisfy a variety of needs, such as being informed, entertained and inspired, or connected to their friends and community. Hence, to design a ranking function that gives useful and personalized post recommendations, it would be helpful to be able to predict the affective response a user may have to a post (e.g., entertained, informed, angered). This paper describes the challenges and solutions we developed to apply Affective Computing to social media recommendation systems.We address several types of challenges. First, we devise a taxonomy of affects that was small (for practical purposes) yet covers the important nuances needed for the application. Second, to collect training data for our models, we balance between signals that are already available to us (namely, different types of user engagement) and data we collected through a carefully crafted human annotation effort on 800k posts. We demonstrate that affective response information learned from this dataset improves a module in the recommendation system by more than 8%. Online experimentation also demonstrates statistically significant decreases in surfaced violating content and increases in surfaced content that users find valuable. CCS CONCEPTS• Human-centered computing → Social networks; • Information systems → Sentiment analysis; Recommender systems.

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Topological to deep learning era for identifying influencers in online social networks :a systematic review

Rashid

Bhat

2023

Multimed Tools Appl

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Detecting Inspiring Content on Social Media

Cited by 6 publications

References 42 publications

BLUE at Memotion 2.0 2022: You have my Image, my Text and my Transformer

BLUE at Memotion 2.0 2022: You have my Image, my Text and my Transformer

Affective Signals in a Social Media Recommender System

Topological to deep learning era for identifying influencers in online social networks :a systematic review

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