About Challenges in Data Analytics and Machine Learning for Social Good
- Challenge 1 (C1)—Dataset acquisition and preparation:The acquisition of datasets is always a complex matter, for instance in scenarios such as the medical one due to ethical issues, but also in others due to data confidentiality. Moreover, from a technical point of view, extracting the required data in large quantities and from multiple sources, while also preparing it (e.g., feature extraction, dataset balancing, etc.) in such a way to properly support the specific machine learning tasks, is often a challenge in itself.
- Challenge 2 (C2)—Output interpretation:Designing and building the model(s), choosing the most suitable and effective ML algorithm(s), performing the tests and evaluating effectiveness results are only the first steps in these contexts. Interpretation of the results in terms of the reasons why ML models produced specific results are key to advancing knowledge and obtaining the complex goals envisioned in each scenario.
2. Machine Learning for Social Good
2.1. Scenario 1: Social Network Analytics
2.2. Scenario 2: Game Analytics
2.3. Scenario 3: Medical Analytics
2.4. Scenario 4: Human Resource Management (Hrm) Analytics
3. Challenges in the Social Network Analytics/Cultural Heritage Scenario
3.1. Challenge 1—Dataset Acquisition and Preparation
- Tweet topic feature: Reading some of the tweets in the dataset, we observed that museums have a limited number of different tweet topics. Therefore, we decided to take advantage of this particular situation for the GOOD/BAD classification by adding the relevance of a tweet to topic classes. This step in the dataset preparation is new with respect to the proposal presented in .
- Artwork: description/presentation of some piece of art;
- Festivities greetings;
- Historical celebrations or facts related to artworks;
- Happened #OnThisDay;
- Museum promotions;
- Important historical people or their citations;
- Miscellany: tweets not falling into other classes.
- we counted how many words of the corresponding word list are contained in the tweet;
- we counted how many entities of the corresponding entity list are contained in the tweet;
- we then normalized the counts with respect to the lists lengths, and summed the resulting numbers.
- Features extraction: We identified a set of features to be used for GOOD/BAD classification. Some of these features are content-dependent (e.g., number of mentions and URLs in the message, the length of the message, if the post contains an image or not, and so on), others are context dependent (e.g., the time of the day in which the post was published or if it is a retweet or not). The complete list of content and context features might be found in . Moreover, we added one feature per topic and the feature value as explained before.
- Grouping: As we said, we measure the success of a tweet by the numbers of likes and retweets it received. However, it is not possible to define the same thresholds to these numbers for all museums. Indeed, the best tweet posted by a small museum might have the same number of likes (or even less) of the worst post of a very large museums. Therefore, we divided the museums into three groups according to the number of followers of their Twitter accounts, grouping together museums with similar numbers. Tweets in the dataset were divided accordingly into three groups.
3.2. Challenge 2—Output Interpretation
- Tweet Classification: We classify tweets into GOOD and BAD by considering the content and context features, and the topic class features. We evaluated different classifiers to identify the best performing one (XGboost) and obtained the promising accuracy results shown in Table 1.
- Model interpretation: Besides tweets classification, other information can be gathered by interpreting the machine learning algorithm output. We want to understand which feature values contributed the most to tweets being classified into BAD. Such insights are then used to suggest to the museum social media manager how the tweet can be enhanced to make communications (and cultural heritage diffusion) more effective. Suggestions are indications on how to modify features values (e.g., add a mention or remove a URL) are thus easy to give, to understand, and to implement.
4. Challenges in the Game Analytics Scenario
4.1. Challenge 1—Dataset Acquisition and Preparation
- Data acquisition: First of all, the data had to be acquired from the BoardGameGeek—BGG (http://boardgamegeek.com/, accessed on 5 May 2022) website, the current reference for the board game community. The website is based on a very large database which, however, is not directly accessible as a download. Some information is accessible from an API, other information is available only through direct navigation on the website pages. This required the identification of the relevant information from the page of each game, construction of an ad hoc parser exploiting both direct scraping and BGG API to acquire the data from the pages and associated XML data of the first 50,000 games in the BGG ranking. Among the extracted fields, one of the most important is the textual description of the game;
- Data enrichment: In order to possibly enhance the effectiveness of ML classification, the dataset has been enrinched with additional information coming from the rulebook associated to each game. This enrichment process was completely unfeasible to be done manually: in BGG, each game was associated to a community page containing various files, and the rulebook could be “hidden” among dozens of files (which are not categorized in any way and typically vary a lot from game to game in terms of content). In order to solve this, the process was modelled as an ML process itself: a subset of the files (some thousands) were manually identified and labeled as “rulebook” or “other”. Then, based on the file contents, an ML model was built and trained in order to automatically learn if a file was actually a rulebook: this allowed the extension of the whole dataset with a very high accuracy ratio (>95%).
- Pre-processing and feature extraction included elaborating the textual information, performing stopword removal and computing the textual features’ TF-IDF values;
- Resampling was needed since the categories distribution of the dataset was very unbalanced: only the most frequent categories were kept, each with an equal number of samples.
4.2. Challenge 2—Output Interpretation
- Dataset splitting, cross validation: grid search techniques, combined with cross validation, enabled us to perform a significant number of runs and determine the best parameters for the model;
- Different classifiers: Evaluating different classifiers was crucial to determine the best-performing ones for the specific situation; Figure 5 shows the accuracy, precision, recall and f1 levels obtained in the best performing case (Random Forest). As we can see, the accuracy obtained on the “enriched” dataset (also including rulebook information) was quite satisfying (82%);
- Model interpretation: Besides accuracy levels, interpreting the models can lead to interesting knowledge. By analyzing the Shapley values in a summary plot (Figure 6 shows the one for the Fantasy category), it is possible to acquire possibly unknown information about the games and their categories, including the most discriminant features (keywords). For instance, terms such as “dragon” and “land” positively connote the category, while others (e.g., “tank”) are typically not found in its games. More in-depth analyses can also be performed by plotting a dependency plot specific to a particular feature (Figure 7 shows an example of the “secretly” feature for the Cardgame category): since the SHAP value of each sample (vertical axis) grows for increasing TF-IDF scores (horizontal axis), we understand that the concept of “having secret information” is indeed typically found in card games but seldom in others. By exploiting insights coming from analyses such as these, the knowledge about games, game categories and game mechanics can be effectively improved.
5. A Short Overview of the Challenges in the Medical and HRM Analytics Scenarios
5.1. Medical Scenario
5.2. Human Resource Management Scenario
6. Related Works
6.1. Scenario 1: Social Network Analytics
6.2. Scenario 2: Game Analytics
6.3. Scenario 3: Medical Analytics
6.4. Scenario 4: HRM Analytics
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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Martoglia, R.; Montangero, M. About Challenges in Data Analytics and Machine Learning for Social Good. Information 2022, 13, 359. https://doi.org/10.3390/info13080359
Martoglia R, Montangero M. About Challenges in Data Analytics and Machine Learning for Social Good. Information. 2022; 13(8):359. https://doi.org/10.3390/info13080359Chicago/Turabian Style
Martoglia, Riccardo, and Manuela Montangero. 2022. "About Challenges in Data Analytics and Machine Learning for Social Good" Information 13, no. 8: 359. https://doi.org/10.3390/info13080359