SMP Challenge: An Overview and Analysis of Social Media Prediction Challenge

• URL: http://arxiv.org/abs/2405.10497v1
• Date: Fri, 17 May 2024 02:36:14 GMT
• Title: SMP Challenge: An Overview and Analysis of Social Media Prediction Challenge
• Authors: Bo Wu, Peiye Liu, Wen-Huang Cheng, Bei Liu, Zhaoyang Zeng, Jia Wang, Qiushi Huang, Jiebo Luo,
• Abstract summary: Social Media Popularity Prediction (SMPP) is a crucial task that involves automatically predicting future popularity values of online posts. This paper summarizes the challenging task, data, and research progress.
• Score: 63.311045291016555
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Social Media Popularity Prediction (SMPP) is a crucial task that involves automatically predicting future popularity values of online posts, leveraging vast amounts of multimodal data available on social media platforms. Studying and investigating social media popularity becomes central to various online applications and requires novel methods of comprehensive analysis, multimodal comprehension, and accurate prediction. SMP Challenge is an annual research activity that has spurred academic exploration in this area. This paper summarizes the challenging task, data, and research progress. As a critical resource for evaluating and benchmarking predictive models, we have released a large-scale SMPD benchmark encompassing approximately half a million posts authored by around 70K users. The research progress analysis provides an overall analysis of the solutions and trends in recent years. The SMP Challenge website (www.smp-challenge.com) provides the latest information and news.

Related papers

• QuaLLM: An LLM-based Framework to Extract Quantitative Insights from Online Forums [10.684484559041284]
  This study introduces QuaLLM, a novel framework to analyze and extract quantitative insights from text data on online forums. We applied this framework to analyze over one million comments from two Reddit's rideshare worker communities.
  arXiv  Detail & Related papers  (2024-05-08T18:20:03Z)
• Recent Advances in Hate Speech Moderation: Multimodality and the Role of Large Models [52.24001776263608]
  This comprehensive survey delves into the recent strides in HS moderation. We highlight the burgeoning role of large language models (LLMs) and large multimodal models (LMMs) We identify existing gaps in research, particularly in the context of underrepresented languages and cultures.
  arXiv  Detail & Related papers  (2024-01-30T03:51:44Z)
• GPT-4V(ision) as A Social Media Analysis Engine [77.23394183063238]
  This paper explores GPT-4V's capabilities for social multimedia analysis. We select five representative tasks, including sentiment analysis, hate speech detection, fake news identification, demographic inference, and political ideology detection. GPT-4V demonstrates remarkable efficacy in these tasks, showcasing strengths such as joint understanding of image-text pairs, contextual and cultural awareness, and extensive commonsense knowledge.
  arXiv  Detail & Related papers  (2023-11-13T18:36:50Z)
• A Survey on Interpretable Cross-modal Reasoning [64.37362731950843]
  Cross-modal reasoning (CMR) has emerged as a pivotal area with applications spanning from multimedia analysis to healthcare diagnostics. This survey delves into the realm of interpretable cross-modal reasoning (I-CMR) This survey presents a comprehensive overview of the typical methods with a three-level taxonomy for I-CMR.
  arXiv  Detail & Related papers  (2023-09-05T05:06:48Z)
• ManiTweet: A New Benchmark for Identifying Manipulation of News on Social Media [74.93847489218008]
  We present a novel task, identifying manipulation of news on social media, which aims to detect manipulation in social media posts and identify manipulated or inserted information. To study this task, we have proposed a data collection schema and curated a dataset called ManiTweet, consisting of 3.6K pairs of tweets and corresponding articles. Our analysis demonstrates that this task is highly challenging, with large language models (LLMs) yielding unsatisfactory performance.
  arXiv  Detail & Related papers  (2023-05-23T16:40:07Z)
• Contextualizing Emerging Trends in Financial News Articles [2.9483477138814287]
  We focus on emerging trends detection in financial news articles about Microsoft, collected before and during the start of the COVID-19 pandemic. We make the dataset accessible and propose a strong baseline for exploring the dynamics of similarities between pairs of keywords. We evaluate against a gold standard (Google Trends) and present noteworthy real-world scenarios regarding the influence of the pandemic on Microsoft.
  arXiv  Detail & Related papers  (2023-01-20T12:56:52Z)
• Time-aware topic identification in social media with pre-trained language models: A case study of electric vehicles [1.2891210250935146]
  We propose a time-aware topic identification approach with pre-trained language models. The proposed approach consists of two stages: the dynamics-focused function for tracking time-varying topics with language models and the emergence-scoring function to examine future promising topics.
  arXiv  Detail & Related papers  (2022-10-11T04:50:10Z)
• DFW-PP: Dynamic Feature Weighting based Popularity Prediction for Social Media Content [4.348651617004765]
  Over-saturation of content on social media platforms has persuaded us to identify the important factors that affect content popularity. We propose the DFW-PP framework, to learn the importance of different features that vary over time. The proposed method is experimented with a benchmark dataset, to show promising results.
  arXiv  Detail & Related papers  (2021-10-16T08:40:58Z)
• Two-Faced Humans on Twitter and Facebook: Harvesting Social Multimedia for Human Personality Profiling [74.83957286553924]
  We infer the Myers-Briggs Personality Type indicators by applying a novel multi-view fusion framework, called "PERS" Our experimental results demonstrate the PERS's ability to learn from multi-view data for personality profiling by efficiently leveraging on the significantly different data arriving from diverse social multimedia sources.
  arXiv  Detail & Related papers  (2021-06-20T10:48:49Z)
• Evolving Methods for Evaluating and Disseminating Computing Research [4.0318506932466445]
  Social and technical trends have significantly changed methods for evaluating and disseminating computing research. Traditional venues for reviewing and publishing, such as conferences and journals, worked effectively in the past. Many conferences have seen large increases in the number of submissions. Dis dissemination of research ideas has become dramatically through publication venues such as arXiv.org and social media networks.
  arXiv  Detail & Related papers  (2020-07-02T16:50:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.