| Abstract | Through experimentation with a range of years, we found that the birth dates of students in college at the time when social media such as AIM, SMS text messaging, MySpace and Facebook first became popular, enable accurate age prediction. |
| Experiments and Results | 5.2 Social Media and Generation Y |
| Experiments and Results | We were motivated to examine these years due to the emergence of social media technologies during that time. |
| Experiments and Results | Generation Y is considered the social media generation, so we decided to examine how the creation and/ or popularity of social media technologies compared to the years that had a change in writing style. |
| Introduction | The users of these social media platforms have created their own form of unstructured writing that is best characterized as informal. |
| Introduction | social media generation. |
| Introduction | We focus on this generation due to the rise of popular social media technologies such as messaging and online social networks sites that occurred during that time. |
| Related Work | Their work shows that ease of classification is dependent in part on what division is made between age groups and in turn motivates our decision to study whether the creation of social media technologies can be used to find the dividing line(s). |
| Conclusion | We presented a novel model for record extraction from social media streams such as Twitter. |
| Evaluation | While our experiments utilized binary relations, we believe our general approach should be useful for nary relation recovery in the social media domain. |
| Introduction | We propose a method for discovering event records from social media feeds such as Twitter. |
| Introduction | Social media messages are often short, make heavy use of colloquial language, and require situational context for interpretation (see examples in Figure 1). |
| Introduction | Social Media Feeds |
| Introduction | In this paper, we focus on the computational analysis of collective discourse, a collective behavior seen in interactive content contribution and text summarization in online social media . |
| Introduction | In social media , discourse (Grosz and Sidner, 1986) is Often a collective reaction to an event. |
| Prior Work | Finally, recent research on analyzing online social media shown a growing interest in mining news stories and headlines because of its broad applications ranging from “meme” tracking and spike detection (Leskovec et al., 2009) to text summarization (Barzilay and McKeown, 2005). |