twitter_analysis   153

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Analyzing Discourse Communities with Distributional Semantic Models
This paper presents a new corpus-driven approach applica- ble to the study of language patterns in social and political contexts, or Critical Discourse Analysis (CDA) using Distri- butional Semantic Models (DSMs). This approach considers changes in word semantics, both over time and between com- munities with differing viewpoints. The geometrical spaces constructed by DSMs or “word spaces” offer an objective, robust exploratory analysis tool for revealing novel patterns and similarities between communities, as well as highlight- ing when these changes occur. To quantify differences be- tween word spaces built on different time periods and from different communities, we analyze the nearest neighboring words in the DSM, a process we relate to analyzing “concor- dance lines”. This makes the approach intuitive and inter- pretable to practitioners. We demonstrate the usefulness of the approach with two case studies, following groups with opposing political ideologies in the Scottish Independence Referendum, and the US Midterm Elections 2014.
Twitter_Analysis  NLTK 
september 2015 by ewan
Towards Decision Support and Goal Achievement: Identifying Action-Outcome Relationships From Social Media
In this paper, we investigate the feasibility of mining the relationship between actions and their outcomes from the aggregated timelines of individuals posting experiential mi- croblog reports. Our contributions include an architecture for extracting action-outcome relationships from social me- dia data, techniques for identifying experiential social media messages and converting them to event timelines, and an analysis and evaluation of action-outcome extraction in case studies.
Twitter_Analysis  Sentiment_Analysis 
july 2015 by ewan
Suspended Accounts in Retrospect: An Analysis of Twitter Spam
In this study, we examine the abuse of online social networks at the hands of spammers through the lens of the tools, techniques, and support infrastructure they rely upon. To perform our analysis, we identify over 1.1 million accounts suspended by Twitter for disrup- tive activities over the course of seven months. In the process, we collect a dataset of 1.8 billion tweets, 80 million of which belong to spam accounts. We use our dataset to characterize the behavior and lifetime of spam accounts, the campaigns they execute, and the wide-spread abuse of legitimate web services such as URL shorten- ers and free web hosting. We also identify an emerging marketplace of illegitimate programs operated by spammers that include Twitter account sellers, ad-based URL shorteners, and spam affiliate pro- grams that help enable underground market diversification.
Twitter_Analysis 
march 2015 by ewan
Using social network graph analysis for interest detection
A person’s interests exist as an internal state and are difficult to de- fine. Since only external actions are observable, a proxy must be used that represents someone’s interests. Techniques like collaborative filtering, be- havioral targeting, and hashtag analysis implicitly model an individual’s interests. I argue that these models are limited to shallow, temporary interests, which do not reflect people’s deeper interests or passions. I pro- pose an alternative model of interests that takes advantage of a user’s social graph. The basic principle is that people only follow those that interest them, so the social graph is an effective and robust proxy for people’s interests.
Twitter_Analysis 
october 2014 by ewan

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big-data  clustering  javascript  linked_data  livinglab  nlp/nltk  nltk  ogd  probability_&_stats  python  r  sentiment_analysis  visualization 

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