Investigation of the Gender-Specific Discourse about Online Learning during COVID-19 on Twitter Using Sentiment Analysis, Subjectivity Analysis, and Toxicity Analysis
Abstract
:1. Introduction
1.1. Twitter: A Globally Popular Social Media Platform
1.2. Gender Diversity on Social Media Platforms
2. Literature Review
2.1. A Brief Review of Recent Works Related to Sentiment Analysis of Tweets about COVID-19
2.2. Review of Recent Works Related to Data Mining and Analysis of Tweets about Online Learning during COVID-19
3. Methodology
3.1. Data Description
3.2. System Design and Development
- (a)
- Removal of characters that are not alphabets.
- (b)
- Removal of URLs
- (c)
- Removal of hashtags
- (d)
- Removal of user mentions
- (e)
- Detection of English words using tokenization.
- (f)
- Stemming.
- (g)
- Removal of stop words
- (h)
- Removal of numbers
Algorithm 1: Data Preprocessing |
Input: Dataset Output: New Attribute of Preprocessed Tweets File Path Read data as dataframe English words: nltk.download(‘words’) Stopwords: nltk.download(‘stopwords’) Initialize an empty list to store preprocessed text corpus[] for i from 0 to n do Obtain Text of the Tweet (‘text’ column) text = re.sub(‘[^a-zA-Z]’, whitespace, string) text = re.sub(r‘http\S+’, '', string) text = text.lower() text = text.split() ps = PorterStemmer() all_stopwords = english stopwords text = ps.stem(word) for word in text if not in all_stopwords text = whitespace.join(text) text = whitespace.join(re.sub(“(#[A-Za-z0-9]+)| (@[A-Za-z0-9]+)|([^0-9A-Za-z\t])| (\w+:\/\/\S+)", whitespace, string).split()) text = whitespace.join(if c.isdigit() else c for c in text) text = whitespace.join(w for w in wordpunct_tokenize(text) if w.lower() in words) corpus ← append(text) End of for loop New Attribute ← Preprocessed Text (from corpus) |
Algorithm 2: Detect Gender from Twitter Usernames |
Input: Dataset Output: File with the Gender of each Twitter User File Path Read data as dataframe procedure PredictGender (csv file) gp ← Initialize GenderPerformr output_file ← Initialize empty text file regex ← Initialize RegEx df ← Read csv file into Dataframe for each column in df do if column is user_name column then name_values ← Extract values of the column end if End of for loop for each name in name_values do if name is ”null”, ”nan”, empty, or None then write name and ”None” to Gender else if name does not match RegEx then write name to output file count number of words in name if words > 1 then splittedname ← split name by spaces name ← First element of splittedname end if str result ← Perform gender prediction using gp gender ← str result extract gender if gender is “M” then write ”Male” to Gender else if gender is ”F” then write ”Female” to Gender else if gender is empty or whitespace then write ”None” to Gender else if name in lowercase exists in set of english words then write ”None” to Gender else write ”Maybe” to Gender end if else write name and ”None” to Gender end if End of for loop End of procedure Write df with a new “Gender” attribute to a new .CSV file Export .CSV file |
Algorithm 3: Detect Sentiment of Tweets Using VADER |
Input: Preprocessed Dataset (output from Algorithm 1) Output: File with Sentiment of each Tweet File Path Read data as dataframe Import VADER sid obj ← Initialize SentimentIntensityAnalyzer for each row in df[‘PreprocessedTweet’] do tweet_text ← df[‘PreprocessedTweet’][row] if tweet_text is null then sentiment score ← 0 else sentiment_dict = sid_obj.polarity_scores(df[‘PreprocessedTweet’][row]) compute sentiment_dict[‘compound’] sentiment score ← compound sentiment end if if sentiment score >= 0.05 then sentiment ← ‘positive’ else if sentiment score <= −0.05 then sentiment ← ‘negative’ else sentiment ← ‘neutral’ end if df [row] ← compound sentiment and sentiment score End of for loop Write df with new attributes – sentiment class and sentiment score to a new .CSV file Export .CSV file |
Algorithm 4: Detect Sentiment of Tweets Using Afinn |
Input: Preprocessed Dataset (output from Algorithm 1) Output: File with Sentiment of each Tweet File Path Read data as dataframe Import Afinn afn ← Instantiate Afinn for each row in df[‘PreprocessedTweet’] do tweet_text ← df[‘PreprocessedTweet’][row] if tweet_text is null then sentiment score ← 0 else apply afn.score() to df[‘PreprocessedTweet’][row] sentiment score ← afn.score(df[‘PreprocessedTweet’][row]) end if if sentiment score > 0 then sentiment ← ‘positive’ else if sentiment score < 0 then sentiment ← ‘negative’ else sentiment ← ‘neutral’ end if df [row] ← sentiment and sentiment score End of for loop Write df with new attributes – sentiment class and sentiment score to a new .CSV file Export .CSV file |
Algorithm 5: Detect Polarity and Subjectivity of Tweets Using TextBlob |
Input: Preprocessed Dataset (output from Algorithm 1) Output: File with metrics for polarity and subjectivity of each Tweet File Path Read data as dataframe Import TextBlob Initialize Lists for Blob, Polarity, Subjectivity, Polarity Class, and Subjectivity Class for row in df[‘PreprocessedTweet’] do convert item to TextBlob and append to Blob List End of for loop for each blob in Blob List do for each sentence in blob do calculate polarity and subjectivity append them to Polarity and Subjectivity Lists respectively End of for loop End of for loop for each value in Polarity List do if (p > 0): pclass.append(‘Positive’) else if (p < 0): pclass.append(‘Negative’) else: pclass.append(‘Neutral’) end if End of for loop for each value in Subjectivity List do if (s > 0.6): sclass.append(‘Highly Opinionated’) else if (s < 0.4): sclass.append(‘Least Opinionated’) else: sclass.append(‘Neutral’) end if End of for loop Write df with new attributes - polarity, polarity class, subjectivity, and subjectivity class to a new CSV file Export .CSV file |
Algorithm 6: Perform Toxicity Analysis of the Tweets Using Detoxify |
Input: Preprocessed Dataset (output from Algorithm 1) Output: File with metrics of toxicity for each Tweet File Path Read data as dataframe Import Detoxify Instantiate Detoxify predictor = Detoxify(‘multilingual’) Initialize Lists for toxicity, obscene, identity attack, insult, threat, and sexually explicit for each row in df[‘PreprocessedTweet’] do apply predictor.predict() to df[‘PreprocessedTweet’][row] data ← predictor.predict (df[‘PreprocessedTweet’][row]) toxic_value = data[‘toxicity’] obscene_value = data['obscene’] identity_attack_value = data[‘identity_attack’] insult_value = data[‘insult’] threat_value = data[‘threat’] sexual_explicit_value = data[‘sexual_explicit’] append ← lists for toxicity, obscene, identity attack, insult, threat, sexually explicit score [] ← toxicity, obscene, identity attack, insult, threat, and sexually explicit max_value = maximum value in Score[] label = class for max_value append values to the corpus End of for loop data = [] for each i from 0 to n do: create an empty list tmp append tweet id, text, score[],max_value, and label to tmp append tmp to data End of for loop Write new attributes - toxicity, obscene, identity attack, insult, threat, and sexually explicit, and label to a new CSV file Export .CSV file |
Algorithm 7: Compute the Average Activity of different Genders on a monthly basis |
Input: Preprocessed Dataset (output from Algorithm 1) Output: Average Activity per gender per month File Path Read data as dataframe Initialize lists for distinct males, distinct females, and distinct none for each row in df[‘created_at’] do extract month and year append data End of for loop Create new attribute month_year to hold month and year for each month in df[‘month_year’] do d_males = number of distinct males based on df[‘user_id’] and df[‘gender’] d_females = number of distinct females based on df[‘user_id’] and df[‘gender’] d_none = calculate number of distinct none based on df[‘user_id’] and df[‘gender’] for each male in d_males activity = author Tweets count + author favorites count males_total_activity = males_total_activity + activity End of for loop males_avg_activity = males_total_activity/d_males for each female in d_females activity = author Tweets count + author favorites count females_total_activity = females_total_activity + activity End of for loop females_avg_activity = females_total_activity/d_females for each none in d_none activity = author Tweets count + author favorites count none_total_activity = none_total_activity + activity End of for loop none_avg_activity = none_total_activity/d_none End of for loop |
Algorithm 8: Detect Locations of Twitter Users, Visualize Gender-Specific Tweeting Patterns |
Input: Dataset Output: File with locations (country) of each user, visualization of gender-specific tweeting patterns File Path Read data as dataframe Import PyCountry Import Folium Import Geodata data package for each row in df[‘user_location’] do location_values = columnSeriesObj.values End of for loop For each location in location_values if location is “null”, “nan”, empty, or None then country = none else if spaces = location.count(‘ ’) if (spaces > 0): for word in location.split(): country = pycountry.countries.search_fuzzy(word) defaultcountry = country.name if (spaces = 0) country = pycountry.countries.search_fuzzy(location) end if append values to corpus End of for loop write new attribute “country” to the dataset df pivotdata ← “user location” as the index and “Gender” as attributes pivotdata [attributes] ← “Female”, “Male”, and “None” pivot data [total] ← add “Male”, “Female”, and “None” columns Instantiate Folium map m define threshold scale ← list of threshold values for colored bins choropleth layer ← custom color scale, ranges, and opacity pivotdata [key] ← mapping legend name ← pivotdata [attributes] GenerateMap() |
4. Results and Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A
References
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Social Media Platform | Percentage of Male Users | Percentage of Female Users |
---|---|---|
63 | 37 | |
51.8 | 48.2 | |
Tumblr | 52 | 48 |
53.2 | 46.7 | |
53.5 | 46.5 | |
Quora | 55 | 45 |
56.3 | 43.7 | |
57.2 | 42.8 | |
Telegram | 58.6 | 41.4 |
Sina Weibo | 51 | 49 |
51.7 | 48.3 | |
SnapChat | 48.2 | 51 |
Characteristics of Tweets Analyzed | Tweets Posted by Males | Tweets Posted by Females | Tweets Posted by None |
---|---|---|---|
Positive Tweets (as per VADER) | 25.402% | 21.403% | 53.196% |
Negative Tweets (as per VADER) | 24.457% | 22.801% | 52.742% |
Neutral Tweets (as per VADER) | 22.214% | 14.179% | 63.608% |
Positive Tweets (as per Afinn) | 23.653% | 19.270% | 57.077% |
Negative Tweets (as per Afinn) | 24.227% | 18.663% | 57.110% |
Neutral Tweets (as per Afinn) | 25.037% | 21.475% | 53.488% |
Positive Tweets (as per TextBlob) | 23.529% | 21.168% | 55.303% |
Negative Tweets (as per TextBlob) | 22.905% | 20.494% | 56.602% |
Neutral Tweets (as per TextBlob) | 27.894% | 15.535% | 56.572% |
Characteristics of Tweets Analyzed | Tweets Posted by Males | Tweets Posted by Females | Tweets Posted by None |
---|---|---|---|
Highly opinionated Tweets (as per TextBlob) | 26.094% | 27.735% | 51.171% |
Least opinionated Tweets (as per TextBlob) | 23.618% | 18.355% | 58.027% |
Neutral opinionated Tweets (as per TextBlob) | 24.545% | 21.165% | 52.291% |
Tweets in the toxicity class (as per Detoxify) | 23.680% | 20.119% | 56.201% |
Tweets in the obscene class (as per Detoxify) | 34.184% | 14.483% | 51.334% |
Tweets in the identity attack class (as per Detoxify) | 22.339% | 21.045% | 56.616% |
Tweets in the insult class (as per Detoxify) | 25.923% | 14.936% | 59.142% |
Tweets in the threat class (as per Detoxify) | 25.000% | 0.000% | 75.000% |
Tweets in the sexually explicit class (as per Detoxify) | 5.556% | 27.778% | 66.667% |
Work | Text Analysis of Tweets about Online Learning during COVID-19 | Sentiment Analysis of Tweets about Online Learning during COVID-19 | Analysis of Types of Toxic Content in Tweets about Online Learning during COVID-19 | Subjectivity Analysis of Tweets about Online Learning during COVID-19 |
---|---|---|---|---|
Sahir et al. [71] | ✓ | |||
Althagafi et al. [72] | ✓ | |||
Ali et al. [73] | ✓ | ✓ | ||
Alcober et al. [74] | ✓ | |||
Remali et al. [75] | ✓ | |||
Senadhira et al. [76] | ✓ | ✓ | ||
Lubis et al. [77] | ✓ | ✓ | ||
Arambepola [78] | ✓ | ✓ | ||
Isnain et al. [79] | ✓ | ✓ | ||
Aljabri et al. [80] | ✓ | ✓ | ||
Asare et al. [81] | ✓ | ✓ | ✓ | |
Mujahid et al. [82] | ✓ | ✓ | ||
Al-Obeidat et al. [83] | ✓ | |||
Waheeb et al. [84] | ✓ | ✓ | ||
Rijal et al. [85] | ✓ | |||
Martinez et al. [86] | ✓ | |||
Thakur et al. [this work] | ✓ | ✓ | ✓ | ✓ |
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© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Thakur, N.; Cui, S.; Khanna, K.; Knieling, V.; Duggal, Y.N.; Shao, M. Investigation of the Gender-Specific Discourse about Online Learning during COVID-19 on Twitter Using Sentiment Analysis, Subjectivity Analysis, and Toxicity Analysis. Computers 2023, 12, 221. https://doi.org/10.3390/computers12110221
Thakur N, Cui S, Khanna K, Knieling V, Duggal YN, Shao M. Investigation of the Gender-Specific Discourse about Online Learning during COVID-19 on Twitter Using Sentiment Analysis, Subjectivity Analysis, and Toxicity Analysis. Computers. 2023; 12(11):221. https://doi.org/10.3390/computers12110221
Chicago/Turabian StyleThakur, Nirmalya, Shuqi Cui, Karam Khanna, Victoria Knieling, Yuvraj Nihal Duggal, and Mingchen Shao. 2023. "Investigation of the Gender-Specific Discourse about Online Learning during COVID-19 on Twitter Using Sentiment Analysis, Subjectivity Analysis, and Toxicity Analysis" Computers 12, no. 11: 221. https://doi.org/10.3390/computers12110221