Discussion Graphs: Putting Social Media Analysis in Context

By Emre Kıcıman, Scott Counts, Michael Gamon, M. de Choudhury, Bo Thiesson. 2014

7 citations recorded by the source corpus.

Much research has focused on studying complex phenomena through their reflection in social media, from drawing neighborhood boundaries to inferring relationships between medicines and diseases. While it is generally recognized in the social sciences that such studies should be conditioned on gender, time and other confounding factors, few of the studies that attempt to extract information from social media actually condition on such factors due to the difficulty in extracting these factors from naturalistic data and the added complexity of of including them in analyses. In this paper, we present a simple framework for specifying and implementing common social media analyses that makes it trivial to inspect and condition on contextual information. Our data model---discussion graphs---captures both the structural features of relationships inferred from social media as well as the context of the discussions from which they are derived, such as who is participating in the discussions, when and where the discussions are occurring, and what else is being discussed in conjunction. We implement our framework in a tool called DGT, and present case studies on its use. In particular, we show how analyses of neighborhoods and their boundaries based on geo-located social media data can have drastically varying results when conditioned on gender and time.

Subfields: Artificial Intelligence, Statistical and Nonlinear Physics

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Carlos Castillo
2. Alexandra Olteanu

In Statistical and Nonlinear Physics

1. Alexandra Olteanu
2. Carlos Castillo
3. Emre Kiciman
4. Fernando Diaz
5. Muhammad Imran

Citations in the corpus

• A Critical Review of Online Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2018, background): “As the insights derived from these data are increasingly used to drive policies, to shape products, and for automated decision making, it is becoming imperative to understand the limitations around their use; overlooking them can lead to wrong or inappropriate results [4, 20].”