Xiao, C.; Zhou, F.; Wu, Y.: Predicting audience gender in online content-sharing social networks (2013)
0.00
0.0012881019 = product of:
0.007728611 = sum of:
0.007728611 = weight(_text_:in in 954) [ClassicSimilarity], result of:
0.007728611 = score(doc=954,freq=6.0), product of:
0.059380736 = queryWeight, product of:
1.3602545 = idf(docFreq=30841, maxDocs=44218)
0.043654136 = queryNorm
0.1301535 = fieldWeight in 954, product of:
2.4494898 = tf(freq=6.0), with freq of:
6.0 = termFreq=6.0
1.3602545 = idf(docFreq=30841, maxDocs=44218)
0.0390625 = fieldNorm(doc=954)
0.16666667 = coord(1/6)
- Abstract
- Understanding the behavior and characteristics of web users is valuable when improving information dissemination, designing recommendation systems, and so on. In this work, we explore various methods of predicting the ratio of male viewers to female viewers on YouTube. First, we propose and examine two hypotheses relating to audience consistency and topic consistency. The former means that videos made by the same authors tend to have similar male-to-female audience ratios, whereas the latter means that videos with similar topics tend to have similar audience gender ratios. To predict the audience gender ratio before video publication, two features based on these two hypotheses and other features are used in multiple linear regression (MLR) and support vector regression (SVR). We find that these two features are the key indicators of audience gender, whereas other features, such as gender of the user and duration of the video, have limited relationships. Second, another method is explored to predict the audience gender ratio. Specifically, we use the early comments collected after video publication to predict the ratio via simple linear regression (SLR). The experiments indicate that this model can achieve better performance by using a few early comments. We also observe that the correlation between the number of early comments (cost) and the predictive accuracy (gain) follows the law of diminishing marginal utility. We build the functions of these elements via curve fitting to find the appropriate number of early comments (approximately 250) that can achieve maximum gain at minimum cost.
