#data processing

Seems that EA is now allegedly accused of a mayor privacy violation, having used tracking tools on The Sims FreePlay app to secretly gather and transmit players’ personal information to Facebook for advertising purposes. This data potentially includes unique Facebook IDs, which can be used to match players’ in-game activities to their individual Facebook profiles. Attorneys suspect that these potential data-sharing practices may violate a federal privacy law and are now gathering players to take action.

So, there are at least two class action against EA, because it seems to collect data from players using the Meta Pixel software to harness data from players and sell it to the Meta company, who owns Instagram, Facebook and other social networks.

It would be interesting to learn if this allegations are true and how this would be seen in the eyes of GDPR, European Regulation 679/2016, which allows the processing of personal data only with consent given by the data subjects and also in the context of (online) games.

Consent in the context of the GDPR must be understood as an unambiguous indication of an informed and freely given choice by the data subject, relating to specific processing activities. The burden of proof that these criteria are fulfilled falls upon the controller (i.e., the game developer).

Google Play list the privacy condition of EA for its games, including The Sims Freeplay. Basically EA claims to use players data only to give them "better game experience", which seems vague but not less legit. The only less transparent thing I noticed is that the instructions to opt out of targeted marketing of in-game ads are in English and not in Italian: downloading the game, players allows EA to share their account information with third-party partners to customize advertising experience, which is basically all app developers do, but it's weird that the instruction to opt out doesn't have been translated at all!

This is not the first time EA is accused of, well, unethical commercial practice, since EA has been sentenced to pay fines by Austrian (2023) and Belgian (2018) civil court, because their FIFA loot boxes violated local gambling laws.

Moreover, it's important to notice that in January 2023, the European Parliament adopted a report calling for harmonized EU rules to achieve better player protection in the online video game sector.

The Parliament called for greater transparency from developers about in-game purchases: player should be aware of the type of content before starting to play and during the game. Also, players should be informed on the probabilities in loot box mechanisms, including information in plain language about what algorithms are devised to achieve.

The Parliament further stressed that the proposed legislation should assess whether an obligation to disable in-game payments and loot boxes mechanisms by default or a ban on paid loot boxes should be proposed to protect minors, avoid the fragmentation of the single market and ensure that consumers benefit from the same level of protection, no matter of their place of residence.

The Parliament highlighted problematic practices, including exploiting cognitive biases and vulnerabilities of consumers through deceptive design and marketing, using layers of virtual currencies to mask/distort real-world monetary costs, and targeting loot boxes and manipulative practices towards minors.

Data collection is a fundamental part of any research, business strategy, or decision-making process. Whether you're a student, a professional, or just curious about how data is gathered and used, understanding the basics of data collection can be incredibly useful. In this guide, we'll explore the methods, types, and benefits of data collection in a way that’s easy to understand.

What is Data Collection?

Data collection is the process of gathering information to answer specific questions or to support decision-making. This information, or data, can come from various sources and can be used to make informed decisions, conduct research, or solve problems.

Methods of Data Collection

Surveys and Questionnaires

What Are They? Surveys and questionnaires are tools used to gather information from people. They can be distributed in person, by mail, or online.

How Do They Work? Respondents answer a series of questions that provide insights into their opinions, behaviors, or experiences.

When to Use Them? Use surveys and questionnaires when you need to gather opinions or experiences from a large group of people.

Interviews

What Are They? Interviews involve asking questions to individuals in a one-on-one setting or in a group discussion.

How Do They Work? The interviewer asks questions and records the responses, which can be either structured (with set questions) or unstructured (more conversational).

When to Use Them? Use interviews when you need detailed, qualitative insights or when you want to explore a topic in depth.

Observations

What Are They? Observations involve watching and recording behaviors or events as they happen.

How Do They Work? The observer notes what is happening without interfering or influencing the situation.

When to Use Them? Use observations when you need to see actual behavior or events in their natural setting.

Experiments

What Are They? Experiments involve manipulating variables to see how changes affect outcomes.

How Do They Work? Researchers control certain variables and observe the effects on other variables to establish cause-and-effect relationships.

When to Use Them? Use experiments when you need to test hypotheses and understand the relationships between variables.

Secondary Data Analysis

What Is It? This method involves analyzing data that has already been collected by someone else.

How Does It Work? Researchers use existing data from sources like government reports, research studies, or company records.

When to Use It? Use secondary data analysis when you need historical data or when primary data collection is not feasible.

Types of Data

Quantitative Data

What Is It? Quantitative data is numerical and can be measured or counted.

Examples: Age, income, number of products sold.

Use It When: You need to quantify information and perform statistical analysis.

Qualitative Data

What Is It? Qualitative data is descriptive and involves characteristics that can be observed but not measured numerically.

Examples: Customer feedback, interview responses, descriptions of behavior.

Use It When: You need to understand concepts, opinions, or experiences.

Benefits of Data Collection

Informed Decision-Making

Data provides insights that help individuals and organizations make informed decisions based on evidence rather than guesswork.

Identifying Trends and Patterns

Collecting data allows you to identify trends and patterns that can inform future actions or strategies.

Improving Services and Products

By understanding customer needs and preferences through data, businesses can improve their products and services to better meet those needs.

Supporting Research and Development

Data is crucial for researchers to test hypotheses, validate theories, and advance knowledge in various fields.

Enhancing Efficiency

Data helps in streamlining processes and improving operational efficiency by highlighting areas that need attention or improvement.

Conclusion

Understanding the methods, types, and benefits of data collection can greatly enhance your ability to gather useful information and make informed decisions. Whether you're conducting research, running a business, or just curious about the world around you, mastering data collection is a valuable skill. Use this guide to get started and explore the many ways data can help you achieve your goals.

To know more: A Guide to Data Collection: Methods, Types, and Benefits

Hey everyone, so it is about 8:30pm and I am sure that by the time I write this it will be nearly 9 but I wanted to update everyone who is following my Speechy research on here. I programmed 2 new programs today, a Prosodic Pitch Analyzer (PPA), and an RMS Energy Analyzer using my handy-dandy new favorite library librosa.

Prosodic Pitch Analyzer

The PPA calculates the fundamental frequency (F0) or pitch of an audio signal and visualizes it using a line plot. This is a useful tool for analyzing prosodic features of speech such as intonation, stress, and emphasis.

The code takes an audio file as input, processes it using the librosa library to extract the fundamental frequency / pitch, and then plots the pitch contour using matplotlib.

The output plot shows the pitch contour of the audio signal over time, with changes in pitch represented by changes in the vertical position of the line. The plot can be used to identify patterns in the pitch contour, such as rising or falling intonation, and to compare the pitch contour of different audio signals. The prosodic pitch analyzer can be used to detect changes in pitch, which can be indicative of a neurological speech disorder. For example, a person with ataxic dysarthria, which is caused by damage to the cerebellum, may have difficulty controlling the pitch and loudness of their voice, resulting in variations in pitch that are not typical of normal speech. By analyzing changes in pitch using a tool like the prosodic pitch analyzer, it is possible to identify patterns that are indicative of certain neurological disorders. This information can be used by clinicians to diagnose and treat speech disorders, and to monitor progress in speech therapy.

RMS Energy Analyzer

The program that calculates the energy of a person's speech processes an audio file and calculates the energy of the signal at each time frame. This can be useful for analyzing changes in a person's speech over time, as well as for detecting changes in the intensity or loudness of the speech.

The program uses the librosa library to load and process the audio file, and then calculates the energy of each frame using the root-mean-square (RMS) energy of the signal. The energy values are then plotted over time using the matplotlib library, allowing you to visualize changes in the energy of the speech.

By analyzing changes in energy over time, you can gain insight into how the speech patterns of people with these disorders may differ from those without.

Analysis with PPA

The research that I've been focused on today primarily looked at the speech recording of myself, the mid-stage HD patient with chorea, the late-stage HD patient (EOL), and a young girl with aphasia.

The patient with aphasia had slurred speech and varied rising and falling much like an AD patient. Earlier I saw her ROS and was surprised at the differences between my rate of speech and hers (aphasia v AD)

My rate of speech

The girl with aphasia's rate of speech

So I decided to compare our speech pitches as well and this is what ours looked like side-by-side.

Hers is on the left, mine on the right.

Her pitch seemed to start off higher (unstable though) like mine, but mine fell during my recording and wobbled for a while. She had some drastic pitch differences but mine had around 16 peaks, where hers had around 18-19. Her latter peaks weren't as high frequency as mine, as my frequency peaks ended up mostly very high in the 1600hz or around 1000hz. There is quite a bit of instability in both our pitches though.

Her energy levels in the 15 seconds of speech started off at high-mid energy, then dropped around 1 second in until almost 3 seconds, shot back up and varied in high, high-mid energy, then had several "dips, and higher moments of energy. At the end around 13 seconds she got a huge boost of "gusto" (well.. energy). She had around 7 breaths (noted by the dips / flatlines)

This was mine. It seems like as the 15 seconds went on I started to run out of steam. I wasn't able to keep my energy higher. Mine had around 11 breaths so I was running out of breath eg having a breathier voice more than she was.

Research Conclusion for Today

Although we have quite a bit in common with our speech energy and pitches, our rate of speaking isn't. She used more syllables at a constant rate which made it pretty obvious she had a lot of slurring / overshooting, mine was a lot less syllables and rate of speech was quite slow and varied more than hers. This illustrates my cognitive difficulties and use of placeholder words along with slight slurring.

As far as pitch, seems that we had similar issues with pitch throughout the 15 second clips, mine spiked in the latter when I was getting "wore out" and hers spiked earlier when she had more energy.

Our energy levels differ because although she had moments of energy, I tuckered out pretty quickly.

I hope this helps shed some insight into both aphasia patients and ataxic dysarthria / HD patients speech / some cognitive differences.