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Found 13 papers, 6 papers with code
On the rise of fear speech in online social media
Recently, social media platforms are heavily moderated to prevent the spread of online hate speech, which is usually fertile in toxic words and is directed toward an individual or a community.
Diagnosing Data from ICTs to Provide Focused Assistance in Agricultural Adoptions
Our classifier achieves accuracies ranging from 79% to 90% across the five states, demonstrating its potential for assisting future ethnographic investigations.
Tiplines to Combat Misinformation on Encrypted Platforms: A Case Study of the 2019 Indian Election on WhatsApp
There is currently no easy way to fact-check content on WhatsApp and other end-to-end encrypted platforms at scale.
Claim Matching Beyond English to Scale Global Fact-Checking
We train our own embedding model using knowledge distillation and a high-quality "teacher" model in order to address the imbalance in embedding quality between the low- and high-resource languages in our dataset.
"Short is the Road that Leads from Fear to Hate": Fear Speech in Indian WhatsApp Groups
We observe that users writing fear speech messages use various events and symbols to create the illusion of fear among the reader about a target community.
Urban Dictionary Embeddings for Slang NLP Applications
The choice of the corpus on which word embeddings are trained can have a sizable effect on the learned representations, the types of analyses that can be performed with them, and their utility as features for machine learning models.
Characterising User Content on a Multi-lingual Social Network
In the case of images containing text that cross language barriers, we see that language translation is used to widen the accessibility.
Hot Streaks on Social Media
We show that user impact tends to have certain characteristics: First, impact is clustered in time, such that the most impactful tweets of a user appear close to each other.
Social and Information Networks
WhatsApp, Doc? A First Look at WhatsApp Public Group Data
In this dataset paper we describe our work on the collection and analysis of public WhatsApp group data.
Social and Information Networks
Political Discourse on Social Media: Echo Chambers, Gatekeepers, and the Price of Bipartisanship
By comparing the two, we find that Twitter users are, to a large degree, exposed to political opinions that agree with their own.
Social and Information Networks
Balancing information exposure in social networks
Our goal is to find two sets of nodes to employ in the respective campaigns, so that the overall information exposure for the two campaigns is balanced.
Joint Non-negative Matrix Factorization for Learning Ideological Leaning on Twitter
We model the problem of learning the liberal-conservative ideology space of social media users and media sources as a constrained non-negative matrix-factorization problem.
Social and Information Networks
Quantifying Controversy in Social Media
Unlike previous work, rather than study controversy in a single hand-picked topic and use domain specific knowledge, we take a general approach to study topics in any domain.
Social and Information Networks
