Challenge: Calls to action are effective means of mobilization in social networks, but their potential for censorship and predicting offline protest events has not yet been evaluated.
Approach: They examine the possibility of their automatic detection on historical data from the 2011-2013 protests in Bolotnaya, Russia.
Outcome: The political calls to action can be annotated and detected with relatively high accuracy and have a moderate positive correlation with actual rally attendance.

Similar Papers

Uncovering Agendas: A Novel French & English Dataset for Agenda Detection on Social Media (2024.lrec-main)

Copied to clipboard

Challenge: a social media analysis of online influence campaigns can reveal the sources of agenda setting . annotated data is limited or nonexistent, but there are methods to detect agenda control .
Approach: They propose a method for detecting instances of agenda control through social media . they use a modest corpus of tweets centered on the 2022 french presidential election .
Outcome: The proposed method overcomes the requirement for large annotated training dataset.
Social Convos: Capturing Agendas and Emotions on Social Media (2024.lrec-main)

Copied to clipboard

Challenge: Social media traffic can provide valuable insights into prevailing opinions and social dynamics among different segments of the population.
Approach: They propose a method to extract influence indicators from messages circulating among groups . they build upon the concept of a convo to identify influential authors .
Outcome: The proposed approach extracts influence indicators from messages circulating among groups of users discussing particular topics.
Analyzing Polarization in Social Media: Method and Application to Tweets on 21 Mass Shootings (N19-1)

Copied to clipboard

Challenge: a new framework for studying political polarization in social media is needed to understand how group divisions manifest in language.
Approach: They propose to cluster tweet embeddings to uncover four dimensions of political polarization in social media . their results apply existing lexical methods to analyze 4.4M tweets on 21 mass shootings .
Outcome: The proposed framework generates more cohesive topics than traditional models.
Unveiling the Truth and Facilitating Change: Towards Agent-based Large-scale Social Movement Simulation (2024.findings-acl)

Copied to clipboard

Challenge: Existing methods for simulating social movements encounter challenges in capturing behavior of participants.
Approach: They propose a hybrid framework for social media user simulation wherein users are categorized into two types: core and ordinary users.
Outcome: The proposed framework is able to simulate the behavior of social media users across real-world datasets and demonstrate its effectiveness and flexibility.
Challenges and Opportunities in Information Manipulation Detection: An Examination of Wartime Russian Media (2022.findings-emnlp)

Copied to clipboard

Challenge: Information manipulation campaigns rely on textbased news and social media content, and NLP can be a valuable tool in combating them.
Approach: They propose to use a dataset to examine the use of NLP in public opinion manipulation campaigns in the 2022 Russia-Ukraine war.
Outcome: The proposed dataset contains 38M+ posts from Russian media outlets on Twitter and VKontakte, as well as public activity and responses, immediately preceding and during the 2022 Russia-Ukraine war.
Rumor Detection on Social Media: Datasets, Methods and Opportunities (D19-50)

Copied to clipboard

Challenge: Social media platforms are used for information gathering, but they also lead to the spreading of rumors and fake news.
Approach: This paper presents a comprehensive list of datasets used for rumor detection . it also reviews the important studies based on what types of information they exploit .
Outcome: This paper presents an overview of the recent studies in the rumor detection field . it provides a comprehensive list of datasets used for rumour detection .
SEDTWik: Segmentation-based Event Detection from Tweets Using Wikipedia (N19-3)

Copied to clipboard

Challenge: Recent work on event detection from tweets has focused on localized events or breaking news only.
Approach: They propose to split tweets into segments, extract bursty segments, cluster them, summarize them.
Outcome: The proposed system can detect newsworthy events occurring at different locations of the world from a wide range of categories.
Fact-Checking, Fake News, Propaganda, and Media Bias: Truth Seeking in the Post-Truth Era (2020.emnlp-tutorials)

Copied to clipboard

Challenge: social media has made it easy for everyone to share and spread information online.
Approach: a tutorial will offer an overview of the broad and emerging research area of disinformation . it will focus on the latest developments and research directions .
Outcome: The tutorial will offer an overview of the broad and emerging research area of disinformation . it will focus on the latest developments and research directions .
#YouToo? Detection of Personal Recollections of Sexual Harassment on Social Media (P19-1)

Copied to clipboard

Challenge: a recent study has found that the disclosure of sexual abuse has positive psychological im- pacts.
Approach: They propose to aggregate personal experiences of sexual harassment from Twitter posts to facilitate a better understanding of social media constructs and bring about social change.
Outcome: The proposed model is compared with state-of-the-art models and is based on a three part Twitter-Specific Social Media Language Model.
Toxicity, Morality, and Speech Act Guided Stance Detection (2023.findings-emnlp)

Copied to clipboard

Challenge: Existing studies that focus on stance detection ignore the speech act, toxic, and moral features of tweets or lack an efficient architecture to detect the attitudes across targets.
Approach: They propose a multitasking model that extracts valence, arousal, and dominance aspects hidden in tweets and injects the emotional sense into the embedded text followed by an efficient attention framework to correctly detect the tweet’s stance.
Outcome: The proposed model exploits the toxicity, morality, and speech act features of the tweets to detect the public's stance.

What is GenGO?

GenGO is an NLP powered publication search system. It currenctly indexes 30k+ papers from ACL Anthology, and implements multi-aspect summarization, semantic search, and more!

Information

About
Limitations