Tracking online topics over time: understanding dynamic hashtag communities

Modeling the Dynamic Framing of Controversial Topics in Online Communities

Understanding how online communities frame controversial topics can have significant impacts in public perception, policy action, and social change. I present two neural-network based approaches for automatically detecting how a community frames a topic and how this framing changes over time. The diachronic word embedding model, trained on individual months of data from the online community Red...

Understanding Communities via Hashtag Engagement: A Clustering Based Approach

Change of Topics over Time - Tracking Topics by their Change of Meaning

In this paper we present a new approach to the analysis of topics and their dynamics over time. Given a large amount of news text on a daily basis, we have identified “hotly discussed” concepts by examining the contextual shift between the time slices. We adopt the volatility measure from econometrics and propose a new algorithm for frequency-independent detection of topic drift.

Tracking Meaning Change Over Time: A Dynamic Field Theory Model

Words are often regarded as “slippery customers” (Labov, 1973). First, it is difficult to come up with a fixed content that seems to apply across all uses of a single word (Wittgensten, 1953). Second, the meaning of a word is subject to wider contextual constraints beyond the company it keeps with other words within a sentence. Third, the assumption that a word means a fixed thing is at odds wi...

Understanding topics and sentiment in an online cancer survivor community.

Online cancer communities help members support one another, provide new perspectives about living with cancer, normalize experiences, and reduce isolation. The American Cancer Society's 166000-member Cancer Survivors Network (CSN) is the largest online peer support community for cancer patients, survivors, and caregivers. Sentiment analysis and topic modeling were applied to CSN breast and colo...