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Investigating the Characteristics and Research Impact of
Sentiments in Tweets with Links to Computer Science Research
Pape...
2
Twitter has been used in the context of academia in different ways
1) By Journals
2) By Conferences
3) By Researchers (g...
3
Twitter data has become very important in social media research
Twitter data for research
1. Explicitly Produced
– Tweet...
4
Why do we need to look at sentiments in tweets which contain links
to research papers?
– Twitter metrics are mainly indi...
5
1) Understand the role and nature of sentiments in tweets by
starting with a qualitative analysis of tweets with non-neu...
6
1) The Microsoft Academic Graph (MAG) dataset was used for this study
– February 2016 version
2) Computer science (CS) r...
7
1) The TextBlob library was used for determining the sentiment polarity of
tweets
– Default scoring scale range from -1 ...
8
Sentiment Tweet Count Associated
Paper Count
Likes Count
(μ)
Retweets Count
(μ)
Positive 866 (1.74%) 579 1.15 1.03
Extre...
9
Aspect Keywords Used Example Tweets
Overall
paper
awesome, great,
interesting, fascinating, nice,
new
a paper published ...
10
Aspect Keywords Used Example Tweets
Overall paper
fool, seriously, shit,
terrible, fuck
this is terrible science ignore...
11
Paper Group Citations
Usage
Total
Likes
Count
Retweets
Count
Mendeley
Readers
Altmetric
Score
All sentiments (group 1) ...
12
• Only computer science research papers were
considered in this study
• By the end of 2017, Twitter increased the chara...
13
• Extend this study to other disciplines
• Ascertain the impact of Twitter’s policy change on increase in
the length of...
CTIDF (Common Tweeter Inverse Document Frequency)
Twitter Users
Papers in the dataset
Seed paper Candidate paper
CTIDF = (...
THANK YOU
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Investigating the Characteristics and Research Impact of Sentiments in Tweets with Links to Computer Science Research Papers

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Presentation made during ICADL'18 conference on Nov 21st 2018.

The full paper can be found at this link https://doi.org/10.1007/978-3-030-04257-8_7

Publicada em: Mídias sociais
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Investigating the Characteristics and Research Impact of Sentiments in Tweets with Links to Computer Science Research Papers

  1. 1. Investigating the Characteristics and Research Impact of Sentiments in Tweets with Links to Computer Science Research Papers Aravind Sesagiri Raamkumar, Savitha Ganesan, Keerthana Jothiramalingam, Muthu Kumaran Selva, Mojisola Erdt, Yin-Leng Theng Centre for Healthy and Sustainable Cities (CHESS) Nanyang Technological University, Singapore ICADL 2018 November 21 2018
  2. 2. 2 Twitter has been used in the context of academia in different ways 1) By Journals 2) By Conferences 3) By Researchers (general use) 4) By Any User (with links to research papers) Background
  3. 3. 3 Twitter data has become very important in social media research Twitter data for research 1. Explicitly Produced – Tweet Content: User-mentions, URLs, Hashtags, Likes, Retweets – Tweet Metadata – User Metadata – User Lists – Public Timeline 2. Inferred Data – Networks & Influence – Information Accuracy & Sentiment Background
  4. 4. 4 Why do we need to look at sentiments in tweets which contain links to research papers? – Twitter metrics are mainly indicators of popularity – Need other proxy indicators of interim quality of tweeted papers – Sentiments can be potentially used in formulating recommendations for research papers! Previous studies have shown that the neutral sentiment is discovered predominantly in tweets with links to research papers - 96% in 270 tweets [1] - 94.8% in 1,000 tweets [2] - 81.7% in 487,610 tweets [3] Problem Area
  5. 5. 5 1) Understand the role and nature of sentiments in tweets by starting with a qualitative analysis of tweets with non-neutral sentiments RQ1: How are the sentiments represented in the tweets, in terms of composition, keywords and attributed aspects? 2) Compare the performance of papers with all sentiments against papers with just neutral sentiment in tweets RQ2: How do papers with all three sentiments compare against papers with only neutral sentiments in terms of impact indicators? Research Objectives
  6. 6. 6 1) The Microsoft Academic Graph (MAG) dataset was used for this study – February 2016 version 2) Computer science (CS) related papers were extracted from the MAG dataset using the CS venue entries indexed in DBLP 3) Papers published since 2012 were considered (n=53,831) 4) Data extracted for these papers – Citations count from Scopus – Altmetrics data from Altmetric.com (Aggregated Altmetrics score) – Altmetrics data from PlumX (Views and Downloads) – Tweets from Twitter 5) 13,809 papers had 77,914 tweets 6) Bot and spam accounts were removed 7) Paper titles were removed from tweets 8) Retweets were not considered 9) Finally, 49,849 tweets for 12,967 associated papers Methodology -Dataset Preparation
  7. 7. 7 1) The TextBlob library was used for determining the sentiment polarity of tweets – Default scoring scale range from -1 to +1 – 0.0 corresponds to neutral sentiment by default – Sentiment score range were modified for better accuracy 2) The keywords representing positive and negative sentiments in the tweet along with the corresponding paper aspect were extracted Methodology - Sentiment Identification and Qualitative Analysis Sentiment category Initial sentiment score range Modified sentiment score range Extremely Positive > 0.5 and <= 1.0 > 0.5 and <= 1.0 Positive > 0.0 and <= 0.5 > 0.3 and <= 0.5 Neutral = = 0.0 >= -0.3 and <= 0.3 Negative < 0.0 and >= -0.5 < -0.3 and >= -0.5 Extremely Negative < -0.5 and >= -1.0 < -0.5 and >= -1.0
  8. 8. 8 Sentiment Tweet Count Associated Paper Count Likes Count (μ) Retweets Count (μ) Positive 866 (1.74%) 579 1.15 1.03 Extremely Positive 527 (1.06%) 393 1.65 1.58 Negative 15 (0.03%) 12 1.73 0.93 Extremely Negative 9 (0.02%) 7 0.78 3.33 Neutral 48432 (97.16%) 12791 0.94 0.83 RQ1: Sentiment Stats for the Tweets
  9. 9. 9 Aspect Keywords Used Example Tweets Overall paper awesome, great, interesting, fascinating, nice, new a paper published on cloud biolinux. awesome. [URL] this is really good. simple rules for better figures [URL] great tips with examples Readership good, great, interesting, nice, worth [TH] [TH] the value of draft picks. nerdy but a great read [URL] looks worth a read #ploscompbio: [Paper] [URL] #oxcompbio Review awesome, good, great, interesting, nice adjusting confounders in ranking #biomarkers: a model-based roc approach - #awesome review [TH] [URL] a nice review paper about image segmentation on gpus [TH] [TH] #gpgpu [URL] Work amazing, excellent, impressive, interesting, nice [TH] just read article on usability testing serious games [URL] excellent work. will be sharing with my students. brainbrowser: distributed, web-based neurological #dataviz. impressive work via [TH] w/ [TH] and more. [URL] Study beautiful, best, cool, interesting, nice beautiful study on how the canary sings! relevant to sequence organization in animal behavior in general. [URL] cool study by browning harmer suggesting anxiety disrupts the expectancy learning process (for threat info) - [URL] RQ1: Positive Tweets
  10. 10. 10 Aspect Keywords Used Example Tweets Overall paper fool, seriously, shit, terrible, fuck this is terrible science ignore it: [URL] the article ([URL] even cites wakefield (2002). #roundup has gone and fucked up. - has anybody else seen this published 4/18/13, linking roundup weed killer to... [URL] Study bad, stupid another bad study on narcissism social media [URL] 19 yr use twitter, 35 yr facebook. lots p surveys not comparable this study should be called, facebook making us stupid [URL] Opinion on authors Idiot [TH] here is one. so you are totally [URL] why am i arguing with an idiot who thinks he speaks for science Paper length Stupid keep it long and complicated, stupid. [URL] Paper title Horrible [Paper]. [URL] pevzner group. cool. (but what a horribly overloaded name!) RQ1: Negative Tweets
  11. 11. 11 Paper Group Citations Usage Total Likes Count Retweets Count Mendeley Readers Altmetric Score All sentiments (group 1) 13.52 1765.78 1.83 1.73 91.62 28.85 Only neutral (group 2) 9.09 557.51 0.66 0.56 48.77 3.97 Paper Group Citations Usage Total Likes Count Retweets Count Mendeley Readers Altmetric Score All sentiments (group 1) 5 92 0 0 53 6 Only neutral (group 2) 4 65.5 0 0 30 1.25 RQ2: Comparison of Paper Groups with Impact Indicators Mean values Median values
  12. 12. 12 • Only computer science research papers were considered in this study • By the end of 2017, Twitter increased the character count in tweets to 280 from the earlier 140 characters • Twitter users can now post more descriptive content and tag more users in their tweets Limitations
  13. 13. 13 • Extend this study to other disciplines • Ascertain the impact of Twitter’s policy change on increase in the length of tweets • Utilize the findings for conceptualizing recommendation techniques that use social media data for recommending research papers Future Work
  14. 14. CTIDF (Common Tweeter Inverse Document Frequency) Twitter Users Papers in the dataset Seed paper Candidate paper CTIDF = (1/3) + (1/2) Candidate paper STAGE 1 STAGE 2 Ranking Strategies 1.) Use co-references and co-citations 2) Use citation count 3) Use sentiments (papers that have higher ratio of positive tweets)
  15. 15. THANK YOU

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