Analyzing Public Reactions, Perceptions, and Attitudes during the MPox Outbreak: Findings from Topic Modeling of Tweets
Abstract
:1. Introduction
2. Literature Review
2.1. A Brief Review of Recent Works Related to the Mining and Analysis of Tweets for Interdisciplinary Research
2.2. A Brief Review of Recent Works Related to the Mining and Analysis of Tweets for Healthcare Research
2.3. Review of Recent Works Related to the Mining and Analysis of Tweets about MPox
3. Methodology
3.1. Technical Overview of RapidMiner
- It supplies pre-built “operators” encompassing distinct functions that can be directly employed or customized for the creation and execution of algorithms and applications.
- RapidMiner is developed using Java, which ensures that RapidMiner “workflows” retain the write once run anywhere (WORA) attribute of Java.
- The platform permits the installation of various extensions to facilitate seamless connectivity and integration of RapidMiner “workflows” with other software and hardware environments.
- Scripts developed in programming languages, such as Python and R, can also be imported into a RapidMiner “workflow” to supplement its functionalities.
- The software enables the creation of new “operators” and effortless dissemination of the same within the RapidMiner community.
- RapidMiner consists of “operators” that enable it to establish connections with social media platforms, such as Twitter and Facebook. Such connections facilitate the extraction of tweets, comments, posts, reactions, and other relevant social media interactions.
3.2. Description of the Topic Modeling Architecture for System Design
- Select a multinomial distribution for each topic z from a Dirichlet distribution with parameter .
- For every document d, select a multinomial distribution from a Dirichlet distribution with parameter .
- In document d, for each word w, select a topic z, such that z {1….K} from the multinomial distribution .
- Select w from the multinomial distribution .
3.3. Description of the System Design and Implementation
- (a)
- Removal of characters that are not alphabets (RegEx used: ^a-zA-Z).
- (b)
- Removal of URLs (RegEx used: http\S+).
- (c)
- Removal of hashtags (RegEx used: #[A-Za-z0-9]).
- (d)
- Removal of user mentions (RegEx used: @[A-Za-z0-9]).
- (e)
- Detection of English words using tokenization.
- (f)
- Stemming and Lemmatization.
- (g)
- Removal of stop words.
- (h)
- Removal of numbers.
4. Results and Discussions
- Limited time range of the analyzed Tweets: The time range of the Tweets that were analyzed in these works represents Tweets that were posted only during certain months of the 2022 MPox outbreak. One of the works [109] included Tweets that were posted on the day the first case of the 2022 MPox outbreak was recorded (7 May 2022) but the other work [113] did not. Furthermore, none of these works analyzed Tweets posted after 23 July 2022.
- Limited number of Tweets used for topic modeling: The number of Tweets that were used for topic modeling in these works is 352,182 and 128,037 Tweets, respectively. It is relevant to mention here that the work presented in [113] involved a two-step process. In the first step, the authors analyzed a dataset of 556,402 Tweets about MPox and performed sentiment analysis of those Tweets. The results showed that 128,037 Tweets (23.01%) had a negative sentiment. Thereafter, only these 128,037 Tweets that had a negative sentiment were used for topic modeling in the second step of that study. The number of Tweets used for topic modeling in the previous works represents a fraction of the total number of Tweets that have been posted since the first recorded case of the 2022 Mpox outbreak on 7 May 2022.
- Elimination of a topic from the study: The work of Ng et al. [109] reports that after performing topic modeling, one topic was categorized as “Miscellaneous”, which accounted for 31.1% of the total number of Tweets. The work also reports that this topic was omitted from the results or, in other words, the specific themes or focus areas of conversation reported in that study [109] are based on the analysis of 68.9% of the Tweets only.
- Lack of reporting of metrics to discuss the working or the accuracy of the topic modeling approaches: The average coherence value of an LDA model serves as a key indicator for the determination of the optimal number of topics. At the same time, metrics such as exclusivity, document entropy, number of tokens, and average word length of each topic help to provide a better understanding of the working of the underlying topic modeling approach. The two prior works that exist in this field [109,113] do not report any of these metrics to discuss either the working or the accuracy of the topic modeling approaches that were used.
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Number of Topics | Average Coherence Value |
---|---|
2 | −6.1450 |
3 | −6.0560 |
4 | −4.6730 |
5 | −5.2120 |
6 | −5.7230 |
7 | −5.8700 |
8 | −6.6150 |
9 | −6.8800 |
10 | −6.1840 |
11 | −5.6000 |
12 | −5.4140 |
13 | −5.3280 |
14 | −6.7830 |
15 | −6.0380 |
16 | −5.6930 |
17 | −5.6520 |
18 | −6.5670 |
19 | −6.0470 |
20 | −6.0610 |
21 | −6.3420 |
22 | −5.5790 |
23 | −6.0700 |
24 | −5.9090 |
25 | −6.7030 |
26 | −6.6010 |
27 | −5.9930 |
28 | −5.9870 |
29 | −5.8120 |
30 | −6.0040 |
31 | −5.8810 |
32 | −6.0350 |
33 | −5.8860 |
34 | −6.2010 |
35 | −5.7920 |
36 | −6.0450 |
37 | −6.5680 |
38 | −6.3470 |
39 | −6.1800 |
40 | −6.2180 |
41 | −6.4490 |
42 | −6.1700 |
43 | −6.2120 |
44 | −6.3390 |
45 | −5.8690 |
46 | −6.2330 |
47 | −6.1720 |
48 | −5.9840 |
49 | −6.1210 |
50 | −5.9280 |
Topic | Minimum Confidence Value | Maximum Confidence Value | Average Confidence Value | Standard Deviation of the Confidence Value |
---|---|---|---|---|
Topic 0 | 0.016 | 0.989 | 0.584 | 0.280 |
Topic 1 | 0.002 | 0.940 | 0.083 | 0.180 |
Topic 2 | 0.005 | 0.951 | 0.179 | 0.200 |
Topic 3 | 0.005 | 0.978 | 0.154 | 0.200 |
Tweet # | Original Text of the Tweet |
---|---|
Topic 0, Theme: Views and Perspectives about MPox | |
Tweet #1 | @vancemurphy @pfizer @moderna_tx @US_FDA Well, you know the new thing is monkey pox, right? Vaccines are so yesterday. |
Tweet #2 | Its annoys me how they use pictures of black peoples hands when they discuss monkey pox |
Tweet #3 | The pics of monkey pox looks exactly like shingles |
Tweet #4 | @masthahh1 Are there any stats on the people who have gotten monkey pox? Were they all vaccinated? |
Tweet #5 | Looking at the state of the UK. I’d be more worried about Monkey Pox catching a dose of Englishman! |
Topic 1, Theme: Updates on Cases and Investigations about MPox | |
Tweet #1 | BREAKING: Health department investigating possible monkey pox case in NYC |
Tweet #2 | New York health officials are investigating a potential case of monkeypox after a patient tested positive for the family of viruses associated with the rare illness. |
Tweet #3 | U.S. government officials are placing orders for millions of doses of monkeypox vaccines amid a worldwide outbreak and a possible case in New York City, the Independent reports. |
Tweet #4 | WHO is convening an Emergency Committee meeting out of concern for international spread of monkeypox, a high consequence infection. They will likely discuss whether to declare monkeypox a Public Health Emergency of International Concern (PHEIC) |
Tweet #5 | The UK Health Security Agency said the new cases of the rare monkeypox infection do not have known connections with the previous confirmed cases announced on 14 May and a case on 7 May |
Topic 2, Theme: MPox and the LGBTQIA+ Community | |
Tweet #1 | @CraigbryCraig @BreezerGalway Moneypox has been known about since 1958. Majority of case are in gay males. No need to freak out |
Tweet #2 | . Gay? Had “close” contact with someone whose in the hospital now in Montreal. Apparently majority in Montreal who contracted the Monkey Pox were gay 35–50 year old men. AIDS started in the gay community too. Something about monkeying around… |
Tweet #3 | @jmcrookston Just to be SUPER CLEAR, what I mean by this, is that no, monkeypox isn’t a “gay disease”. I’m queer and super not okay with the way the media is framing this the same way HIV/AIDS was framed in the 70s/80s. |
Tweet #4 | @jeffreyatucker @ezralevant Some knowledge about Monkey pox, it’s mostly for gay. Not a threat. |
Tweet #5 | @EnemyInAState @TimothyVollmer Absolutely agree only other events won’t have the stigma attached which is happening with monkey pox so many people are convinced it’s a gay disease because there’s no context |
Topic 3, Theme: MPox and COVID-19 | |
Tweet #1 | @COVIDnewsfast Transmission of Monkey Pox is not the same as Covid! |
Tweet #2 | MUST WATCH: Amazing Polly exposes 2021 DAVOS pandemic event for a 15 May 2022 release of Monkey-Pox! BOOM! This Monkey Pox, like COVID, is being exploited to push a global government. Amazing Polly catches them. |
Tweet #3 | @ANCParliament What are your plans on preventing Monkey Pox that has “accidentally” been released in the United States of America from coming into South Africa before it becomes a big Issue like COVID-19? |
Tweet #4 | WW3 is on the horizon, COVID-19, and Monkey Pox about to be released into the world we’ll be lucky if any humans survive? Nice work Joe. #LetsGoBrandon |
Tweet #5 | Monkey Pox is coming! Covid did not do the trick. |
Topic | Average Coherence | Average Word Length | Exclusivity | Document Entropy | Tokens |
---|---|---|---|---|---|
Topic 0 | −5.017 | 4 | 0.925 | 12.707 | 2,146,002 |
Topic 1 | −2.000 | 7 | 0.972 | 11.084 | 310,322 |
Topic 2 | −5.730 | 4 | 0.899 | 11.828 | 902,967 |
Topic 3 | −5.946 | 4.667 | 0.855 | 11.758 | 572,057 |
Work | Sentiment Analysis | Content Analysis | Topic Modeling | Dataset Development |
---|---|---|---|---|
Knudsen et al. [104] | ✓ | |||
Zuhanda et al. [105] | ✓ | |||
Ortiz-Martínez et al. [106] | ✓ | |||
Rahmanian et al. [107] | ✓ | |||
Cooper et al. [108] | ✓ | ✓ | ||
Ng et al. [109] | ✓ | |||
Bengesi et al. [110] | ✓ | |||
Olusegun et al. [11] | ✓ | |||
Farahat et al. [112] | ✓ | ✓ | ||
Sv et al. [113] | ✓ | ✓ | ||
Mohney et al. [114] | ✓ | |||
Nia et al. [115] | ✓ | |||
Iparraguirre-Villanueva [116] | ✓ | |||
AL-Ahdal [117] | ✓ |
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Share and Cite
Thakur, N.; Duggal, Y.N.; Liu, Z. Analyzing Public Reactions, Perceptions, and Attitudes during the MPox Outbreak: Findings from Topic Modeling of Tweets. Computers 2023, 12, 191. https://doi.org/10.3390/computers12100191
Thakur N, Duggal YN, Liu Z. Analyzing Public Reactions, Perceptions, and Attitudes during the MPox Outbreak: Findings from Topic Modeling of Tweets. Computers. 2023; 12(10):191. https://doi.org/10.3390/computers12100191
Chicago/Turabian StyleThakur, Nirmalya, Yuvraj Nihal Duggal, and Zihui Liu. 2023. "Analyzing Public Reactions, Perceptions, and Attitudes during the MPox Outbreak: Findings from Topic Modeling of Tweets" Computers 12, no. 10: 191. https://doi.org/10.3390/computers12100191