I was wondering, if a title of an academic journal or conference somehow reflects the (perceived) quality. So I did an experiment.
Setting. The goal is to predict a rating of a journal or conference based only on the title. If we can build a model that would predict the rating accurately, then we can conclude, that the title tells something about the (perceived) quality of the venue.
Data comes from two sources. The first source is rating of publication venues produced by the Federation of Finnish Learned Societies, so called JUFO ranking. Here rating has four levels: 1 - basic, 2 - leading, 3 - top, and 0 - no rating. The second source of data is a list of “predatory” academic journals, compiled by Jeffrey Beall from the University of Colorado Denver. I add these “predatory” journals to the dataset and encode their rating as -1.
A complete dataset with rankings is available here. It consists of 22301 journal/conference titles. The distribution of ratings is: 1% (-1), 7% (0), 73% (1), 14% (2) and 5% (3).
The dataset is split into training and testing subsets (50:50%) at random.
Preprocessing. Input vectors consist of binary indicators, which indicate presence or absence of a word from the dictionary in a given title. The dictionary is formed of 1000 most frequent words in the training data. Then 300 features, that have the strongest relation to the target variable, are selected.
No stemming or stop word removal is used. This is intuitive. If stemming helped, it would mean that journals in one topic should have the same rating. In fact, I expect the opposite. Within each topic there should be better journals, and not so good journals. I tried experimenting with stemming and stop word removal, indeed, that made predictions worse.
Three extra features are used: the number of words in the title, the number of symbols in the title and mean length of word in the title. Intuitively, lower rated journals may have longer titles and more short connecting words.
Predictive models. The task can be approach as multi-class classification, or regression, since the target is ordinal. I tried several options from both, and ended up with Naive Bayes classifier for multivariate Bernoulli models.
Outcome. The classification accuracy is above random, but not very high. The kappa statistic is 20% (0 means the same as random classification, 1 corresponds to perfect classification).
Here are more detailed results. The recall of rating 2-3 journals is quite low, i.e., not much of them are retrieved and identified correctly.
Labels Precision Recall F1-score -1 0.61 0.45 0.52 0 0.52 0.46 0.49 1 0.76 0.95 0.84 2 0.32 0.03 0.05 3 0.27 0.02 0.04 --- Avg. 0.65 0.73 0.66
The results above are based on the assumption that any misclassification is equally bad. We can also compute cost-weighted classification error, where the cost of misclassification is defined as |true - predicted|. For example, misclassifying a journal of rating 2 as rating 1 carries cost of 1, but misclassifying the same journal as rating 0 carries cost 2.
The mean cost weighted error of our classifier is 0.33, while random classifier gives 0.57. This means that our classification is about 50% better than random, so there is some predictive power in the titles after all.
What are the most informative title words? Here are 20 most informative words/features.
#symbols vision promotion av.symb.per.word #words disease nuclear transfer smart neural botany scandinavian limnology za mechanics ergonomics metal surgery mental hospitality