Reading References

Index	Positive	Negative	Use in Current Research
[1]	a) Compares author publishing patterns in journals vs conferences. b) Summarizes different author contribution methods	a) Purely bibliometric b) Combination of citation and contribution can be combined.	Check ideas marked as [3]
[2]	a) Uses an N-gram model for prediction	a)Ground truth is manually labeled. Need mturk for this. b) Unpublished Masters work	a) Sentiments can be useful. b) Current data is not suitable for this
[3]	a) Gives a citation landscape. This can be a title for an RQ in the citemap paper. b) They use a wordle to highlight topics. Can be used for showing topic terms.	a) Papers are only on ESEM. b) Choice of parameters for alpha and beta for topic modeling along with how it was tuned is not mentioned.	a) Top cited papers are shown using cites per year. This can be helpful.

• [1] Fernandes, J. M. (2014). Authorship trends in software engineering. Scientometrics, 101(1), 257-271.
• [2] Athar, A. (2014). Sentiment analysis of scientific citations (No. UCAM-CL-TR-856). University of Cambridge, Computer Laboratory.
• [3] Raulamo-Jurvanen, P., Mantyla, M. V., & Garousi, V. (2015, October). Citation and Topic Analysis of the ESEM papers. In Empirical Software Engineering and Measurement (ESEM), 2015 ACM/IEEE International Symposium on (pp. 1-4). IEEE.