Identifying and Characterizing the Propagation Scale of COVID-19 Situational Information on Twitter: A Hybrid Text Analytic Approach
Abstract
:1. Introduction
- 1.
- RQ1. How to identify and classify the situational information on Twitter?
- 2.
- RQ2. What will be the features with which to predict the propagation scale of these types of information and what will be the predictability nature of those features?
- 3.
- RQ3. How the results of the propagation scale of situational information can assist the relevant authorities in decision making?
- 1.
- The dataset is collected and the situational information identified from COVID-19 Twitter data.
- 2.
- A novel framework is proposed combining machine learning and LIWC lexicon to characterize the propagation scale of situational information.
- 3.
- Each and every aspect of the framework is analyzed with different evaluations of the information scale in the Results and Discussion section.
2. Related Work
2.1. Social Media in Situational Information
2.2. Twitter during Natural Disasters
2.3. Propagation of Crisis Information in Social Media
3. Proposed Framework
- 1.
- Collecting the COVID-19-related datasets consisting of tweets.
- 2.
- Applying pre-processing steps to remove noisy data.
- 3.
- Manually annotating the random 3000 tweets by different annotators according to different situational information categories.
- 4.
- Feature extraction through TF-IDF, as machine learning classifiers need data in the form of feature vectors.
- 5.
- Applying supervised machine learning classifiers and obtaining accuracy scores to check the classification performance of different classifiers.
- 6.
- Choosing the classifiers with the highest classification accuracy score to label the remaining data.
- 7.
- Extracting content, user-related linguistic and cognitive features to predict the propagation scale.
- 8.
- Applying Machine learning regression algorithms to predict the retweeted amount of every situational information separately.
- 9.
- Presenting the results to analyze the propagation scale through ML regression algorithms evaluation parameters such as co-efficient values of all features.
3.1. Data Collection and Description
3.2. Data Pre-Processing
3.2.1. Tokenization and Lemmatization
3.2.2. TF-IDF (Term Frequency—Inverse Document Frequency)
3.2.3. Annotation and Classification of Situational Information
3.2.4. Machine Learning Classifiers
4. Situational Information Propagation Level Prediction
- 1.
- Emotional-related features: effect, positive emotion (posemo), negative emotion (negemo), anxiety (anx), anger and sadness (sad) words in the posts.
- 2.
- Perception type features: perception, seeing, hearing and feeling in the posts.
- 3.
- Affiliation type features: driving, affiliation, achievements, power, rewards and risk in the posts.
- 4.
- Cognitive processes features: certainty(certain) and differentiation(diff).
- 5.
- User-related features: if users are verified or not, followers (log) amount, following (log) amount and NearState.
- 6.
- Content-related features: length of the post (word count), number of retweets (log) and likes on the posts.
5. Discussion
6. Implications
6.1. Theoretical Implications
6.2. Practical Implications
7. Conclusions and Future Work
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Names and Definitions | Manual Label | Counts |
Precautions and care: precautions from the public healthcare authorities to explain the pace of the epidemic, such the need to pay attention to different aspects of the containment measure, such as going out much less, using sanitizers to wash hands, wearing masks in public and responding to the government announcements. | 0 | 199 |
Announcements or Measures: pandemic announcements such as hospital conditions, the number of cases (recovered, infected or dead), measures taken by health departments, medical equipment reserves and the city and state wise tally of cases. | 1 | 946 |
Donated money, goods or services: donations from governments, government-relevant authorities who want to donate goods, money, or services for pandemic prevention and control, healthcare NGO- and health- related volunteer services announcements of donations are also include in this category. | 2 | 63 |
Emotional support to victims: shows of sympathy by public medical teams and health organizations who are supporting people in the US. | 3 | 199 |
Help Seeking: (a) medical institutions, public health care authorities, individuals, etc. seeking support such as demanding human resources in the form of medical workers and individuals seeking medical aid kits, virus test kits etc. (b) Patients want emotional support such as those seeking comfort and who express depression, etc. | 4 | 279 |
Criticizing authorities: criticizing or questioning the government on their performance in handling the pandemic so far, questioning the government’s initiatives or criticizing members of the public who mislead others such as blind supporters of specific political parties, etc. | 5 | 763 |
Non Situational Information: information that does not fall into any of the above stated and defined categories is classified as non situational information. | 6 | 551 |
Name of Algorithm | Mean Accuracy | F1 | Precision | Recall |
SVM | 67% | 69% | 69% | 66% |
SVM (linear kernel) | 70% | 69% | 71% | 68% |
SVM (radial basis kernel) | 77% | 76% | 77% | 73% |
SVM (Sigmoid Kernel) | 57% | 59% | 62% | 59% |
Random Forest | 56% | 61% | 63% | 60% |
Multinomial Naïve Bayes | 55% | 57% | 60% | 56% |
K-nearest neighbor | 54% | 56% | 59% | 55% |
Logistic Regression Classifier | 56% | 57% | 60% | 55% |
Types | Tweets Frequency | Verified Frequency | Follower Frequency | Following Frequency | RT Frequency | Like Frequency |
Type 0: Precaution and Care | 6120 | 610 (9.96%) | 9,104,201 (2266) | 6.131831e +08 (190,183. 549) | 139,431 (51.955) | 440,375 (167) |
Type 1: Announcements or Measures | 33,830 | 4660 (12.1%) | 79,371,572 (5171) | 6.418501e +09 (262,889 0.172) | 1,701,310 (103.765) | 6,172,700 (313) |
Type 2: Donations | 4720 | 535 (11.33%) | 7,316,652 (4159) | 5.059554e +08 (159,384 0.823) | 187,212 (87.31) | 631,910 (267) |
Type 3: Emotional support | 4120 | 459 (11.14%) | 4,549,173 (3863) | 7.197526e +08 (390,513 0.864) | 257,310 (127.86) | 1,071,331 (497) |
Type 4: Help Seeking | 2217 | 229 (10.33%) | 3,325,963 (4221) | 2.318969e +03 (170,904 0.651) | 247,310 (213.146) | 968,137 (771) |
Type 5: Criticizing the Government | 7561 | 529 (7.00%) | 9,833,219 (3746) | 6.758479e +03 (180,936. 129) | 210,221 (170.531) | 1,193,404 (278) |
Type 6: Non Situational Information | 9763 | 1397 (14.1%) | 13,713,097 (3831) | 12.938331e +08 (260,121. 006043) | 893,071 (177.34) | 2,479,301 (810) |
Features | Type 0 | Type 1 | Type 2 | Type 3 | Type 4 | Type 5 |
affect | 4.91 | 3.54 | 3.72 | 3.67 | 4.48 | 4.78 |
posemo | 1.88 | 1.8 | 1.94 | 1.81 | 3.04 | 1.92 |
negemo | 1.94 | 1.72 | 1.84 | 1.91 | 2.89 | 3.05 |
anx | 0.3 | 0.26 | 0.27 | 0.26 | 0.28 | 0.31 |
anger | 0.6 | 0.6 | 0.59 | 0.6 | 0.67 | 0.77 |
sad | 0.34 | 0.31 | 0.35 | 0.33 | 0.38 | 0.34 |
certain | 1.03 | 0.99 | 1.08 | 0.94 | 1.04 | 0.99 |
differ | 1.58 | 1.56 | 1.58 | 1.7 | 1.93 | 1.69 |
percept | 1.34 | 1.38 | 1.34 | 1.34 | 1.21 | 1.43 |
see | 0.58 | 0.64 | 0.66 | 0.59 | 0.56 | 0.69 |
hear | 0.5 | 0.48 | 0.45 | 0.49 | 0.41 | 0.51 |
feel | 0.21 | 0.19 | 0.15 | 0.21 | 0.18 | 0.17 |
affiliation | 1.4 | 1.39 | 1.46 | 1.5 | 1.72 | 1.56 |
achieve | 1.09 | 1.06 | 1.26 | 1.22 | 1.16 | 1.04 |
power | 2.67 | 2.53 | 2.64 | 2.7 | 2.66 | 2.86 |
reward | 0.84 | 0.78 | 0.73 | 0.77 | 0.82 | 0.8 |
risk | 0.74 | 0.68 | 0.73 | 0.69 | 0.78 | 0.73 |
drives | 6.14 | 5.82 | 6.07 | 6.16 | 6.47 | 6.29 |
Likes | 7.13 | 5.43 | 5.04 | 7.73 | 2.46 | 7.31 |
Verified | 0.123 | 0.33 | 0.210 | 0.188 | 0.18 | 0.16 |
Followers (Log) | 11.837 | 9.621 | 10.765 | 10.213 | 10.341 | 9.321 |
Following (Log) | 14.312 | 16.21 | 14.212 | 13.211 | 15.122 | 16.623 |
Near State | 0.039 | 0.036 | 0.049 | 0.036 | 0.049 | 0.058 |
length | 107.3 | 102.12 | 83.2 | 69.53 | 78.2 | 83.4 |
RMSE | Type 0 | Type 1 | Type 2 | Type 3 | Type 4 | Type 5 |
Linear Regression | 6.85 | 3.87 | 2.20 | 6.29 | 1.86 | 1.56 |
Negative Binomial Regression | 0.62 | 0.67 | 0.80 | 0.74 | 0.77 | 0.72 |
Features | Type 0 | Type 1 | Type 2 | Type 3 | Type 4 | Type 5 |
affect | −6.939 | −3.608 | ||||
posemo | 0.11 | 1.943 | 1.776 | 1.11 | ||
negemo | −0.11 | −2.842 | 2.63 | |||
anx | −2.082 | −1.11 | ||||
anger | −1.249 | −1.804 | −2.35 | |||
sad | 2.350 | |||||
certain | 2.673 | −1.388 | ||||
differ | −2.984 | 0.776 | ||||
percept | 0.318 | 0.163 | ||||
see | −4.441 | −1.11 | −1.11 | 1.44 | ||
hear | −3.331 | 1.99 | ||||
feel | −1.943 | −1.11 | 1.44 | |||
affiliation | 1.527 | −3.469 | −1.18 | |||
achieve | −1.35 | −4.09 | ||||
power | 1.55 | |||||
reward | −3.469 | |||||
risk | −1.735 | −4.30 | ||||
drives | −1.665 | 1.457 | 1.41 | |||
Likes (Log) | 2.43 | 2.123 | 3.668 | 1.1 | 3.571 | 1.107 |
Verified (Log) | −1.443 | 2.789 | −1.499 | 6.27 | 2.054 | −5.829 |
Followers (Log) | 0.34 | 0.07 | 0.72 | 0.51 | 0.07 | 0.001 |
Following (Log) | 2.00 | 2.00 | 2.000 | |||
Near State | 0.28 | 0.48 | 0.49 | 0.27 | 0.22 | |
length | 2.041 | 9.481 | 2.12 | 4.31 | 9.853 |
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Wahid, J.A.; Shi, L.; Gao, Y.; Yang, B.; Tao, Y.; Wei, L.; Hussain, S. Identifying and Characterizing the Propagation Scale of COVID-19 Situational Information on Twitter: A Hybrid Text Analytic Approach. Appl. Sci. 2021, 11, 6526. https://doi.org/10.3390/app11146526
Wahid JA, Shi L, Gao Y, Yang B, Tao Y, Wei L, Hussain S. Identifying and Characterizing the Propagation Scale of COVID-19 Situational Information on Twitter: A Hybrid Text Analytic Approach. Applied Sciences. 2021; 11(14):6526. https://doi.org/10.3390/app11146526
Chicago/Turabian StyleWahid, Junaid Abdul, Lei Shi, Yufei Gao, Bei Yang, Yongcai Tao, Lin Wei, and Shabir Hussain. 2021. "Identifying and Characterizing the Propagation Scale of COVID-19 Situational Information on Twitter: A Hybrid Text Analytic Approach" Applied Sciences 11, no. 14: 6526. https://doi.org/10.3390/app11146526
APA StyleWahid, J. A., Shi, L., Gao, Y., Yang, B., Tao, Y., Wei, L., & Hussain, S. (2021). Identifying and Characterizing the Propagation Scale of COVID-19 Situational Information on Twitter: A Hybrid Text Analytic Approach. Applied Sciences, 11(14), 6526. https://doi.org/10.3390/app11146526