Name
Title | ||
|---|---|---|
Identifying Audience Attributes: Predicting Age, Gender and Personality for Enhanced Article Writing. |
Abstract | ||
|---|---|---|
In order to create an effective article, having great content is essential. However, to achieve this, the writer needs to target a specific audience. A target audience refers to a group of readers that a writer intends to reach with his content. Defining a target audience is substantial because it has a direct effect on adjusting writing style and content of the article. Nowadays, writers rely solely on annotated attributes of articles, such as location and language to understand his/her audience. The aim of this work is to identify the audience attributes of articles, especially not-annotated attributes. Among others, this work focuses on the detection of three key audience attributes of related articles: age, gender, and personality.We compare between multiple machine learning classifiers to detect these attributes. Finally, we demonstrate a prototypical application that enables writers to run existing algorithms such as trend detection and showing related articles that are specific to a defined target audience based on the newly detected attributes. |
Year | Venue | Field |
|---|---|---|
2017 | ICCBDC | Trend detection,Writing style,Cognitive psychology,Psychology,Target audience,Personality |
DocType | Citations | PageRank |
Conference | 1 | 0.46 |
References | Authors | |
6 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Raad Bin Tareaf | 1 | 2 | 4.27 |
| Philipp Berger | 2 | 17 | 8.14 |
| Patrick Hennig | 3 | 14 | 7.38 |
| Jaeyoon Jung | 4 | 1 | 0.46 |
| Christoph Meinel | 5 | 2341 | 319.90 |