Dossy

dossy

The Best User Agent for Web Scraping

Най-добрият потребителски агент за уеб скрапинг

Den bedste brugeragent til web-skrabning

El mejor agente de usuario para el web scraping

O mellor axente de usuario para o rascado web

Ajan itilizatè a pi bon pou grate entènèt

סוכן המשתמש הטוב ביותר לגרידת אינטרנט

Webスクレイピングに最適なユーザーエージェント

Cel mai bun agent de utilizator pentru Web Scraping

Web Scraping සඳහා හොඳම පරිශීලක නියෝජිතයා

The Best User Agent yeWeb Scraping

Wakala Bora wa Mtumiaji wa Kuchakachua Wavuti

I-ejenti Yomsebenzisi Engcono Kakhulu Ye-Web Scraping

AccentFold: Enhancing Accent Recognition - Conclusion, Limitations, and References

CulturaX: A High-Quality, Multilingual Dataset for LLMs - Conclusion and References

Social Media and the Battlefield: Human Error in Modern Warfare

Introduction In the ever-evolving landscape of , understanding your target audience has never been more critical. This foundational step is pivotal for deploying effective marketing strategies that resonate with your audience. Enter the , a semi-fictional representation of your ideal customer based on market research and real data. digital marketing concept of buyer personas With the advent of advanced AI tools like ChatGPT, crafting detailed and accurate buyer personas has become more straightforward, offering marketers a significant edge in tailoring their efforts to meet the specific needs of their target segments. Decoding the DNA of Buyer Personas The What and Why of Buyer Personas Buyer personas are at the heart of any customer-centric marketing strategy. They enable businesses to segment their audience into manageable groups, each with distinct characteristics, behaviors, and needs. Imagine buyer personas as the Rosetta Stone of marketing, decoding the cryptic languages spoken by your diverse audience segments. These meticulously crafted dossiers enable you to channel your inner , allowing for precision strikes with personalized content and offerings that hit the bullseye of customer needs and behaviors. marketing ninja The Anatomy of a Masterful Buyer Persona Creating a comprehensive buyer persona requires a deep dive into the customer's world. This includes understanding their current situation, identifying both their physical and emotional pain points, and recognizing their aspirations. Critical to this process is gathering ample customer reviews, which serve as a goldmine of insights into what customers are looking for, what frustrates them, and how they wish their problems could be solved. A well-crafted persona addresses how your product or service transitions the customer from their current state to their desired situation, providing clear answers to how you alleviate their pains and fulfill their needs. Embark on a data-driven odyssey, delving into the psyche of your customers through a treasure trove of reviews and interactions. I recommend using a product listing website like Amazon. Here, you have to find a similar product category like gardening, and collect a minimum of 25 to 30 customer reviews. Remember, more reviews with different words will help you get a better idea of the customer. The Role of AI in Crafting Buyer Personas Harnessing the Power of AI revolutionizes persona creation by efficiently analyzing vast amounts of customer review data to identify patterns, problems, and desires. This AI tool can distill complex information into actionable insights, helping marketers craft personas that truly reflect the target audience's needs and wants. ChatGPT This isn't just about understanding what your audience says but decoding the subtext of their digital footprints. With ChatGPT, you're not just spotting trends, you're anticipating desires, and crafting personas with the precision of a master jeweler setting the crown jewels. Try adding this prompt that I use: Think like an e-commerce product marketing expert. I have reviews of customers that I have provided below; I want you to go through all of them and generate the following details: 5 pain point sentences that the product has solved in less than 10 words 10 common words that customers use to describe the usefulness/benefit of the product What are areas in life that customers use this product for 10 benefits with marketing angle to advertise to potential customers 10 brief sales phrases in less than 6 words to use when advertising this product 10 questions that can be asked to the potential targets and direct them to their pain points for selling this product :
[Reviews to be added here] Reviews Step-by-Step Guide to Creating Buyer Personas With ChatGPT Data Preparation: The Quest Begins Arm yourself with the arsenal of customer feedback and basic ideas about your product. This preparatory phase is akin to strategizing for battle, where every piece of intelligence is crucial for victory. Interacting With ChatGPT When engaging with ChatGPT, clarity and specificity are key. Guide ChatGPT through the data with queries sharp as swords. For example, inquire about common pain points, desires, and the language customers use to describe their experiences. This approach ensures that the insights generated are both meaningful and actionable. Use the prompt I gave above. Best Practices and Tips Continuous Adaptation The digital realm is an ever-shifting landscape, where today's insights might be tomorrow's ancient history. Regularly revisiting and revitalizing your personas ensures that your strategies remain as agile and potent as a panther in the jungle of the market. Integrating Personas Into Marketing Strategies With your personas as your compass, navigate through the tumultuous seas of and campaigns. Here, personalization isn't just a buzzword; it's your battle cry as you engage customers across the multiverse of digital touchpoints. marketing channels Conclusion The integration of AI, and specifically ChatGPT, into the process of creating buyer personas represents a significant leap forward in marketing efficiency and accuracy. This technology empowers marketers to better understand their audience and craft more personalized, impactful marketing efforts. Embracing AI tools like ChatGPT is not just an option anymore; it's a strategic imperative for those looking to thrive in the competitive digital marketing landscape.

Crafting Buyer Persona With ChatGPT (Essential Step in Marketing)

Async await in is a feature that allows you to write asynchronous code that is easy to read and maintain. It greatly simplifies how you handle asynchronous operations by providing a structured way to await tasks and their results. With the rate at which computers are scaling performance & cores, and our growing need to interface with many external services, async await in C# can make our developer lives easier. C# In the following sections, I’ll highlight 3 beginner tips for using async await in C#. Each tip will be accompanied by a code example, allowing you to see these concepts in action. These cancellation, and performance optimization, so you’ll be off to the races writing async code. tips will cover important aspects such as error handling, Tip 1: Understand the Basics of async await in C# Async await in C# are two special keywords that are used to handle asynchronous code execution. Asynchronous programming enables us to write C# code that can perform multiple operations concurrently, improving the overall performance and responsiveness of an application. In C#, a task represents an asynchronous operation and it’s used to encapsulate a unit of work that can be executed concurrently with other tasks. When using async/await, you can use tasks to handle and manage asynchronous operations in a structured way. To define an async method in C#, you need to add the async modifier to the method signature. For example: public async Task DoAsyncWork()
{
    // Asynchronous code here
} In the above example, the method is declared as async and returns a . This indicates that the method will perform asynchronous operations and can be awaited. DoAsyncWork Task Within the async method, you can use the await keyword to signal the point where the execution should wait for the completion of an asynchronous operation. For example: public async Task DoAsyncWork()
{
    // Perform asynchronous operations
    await Task.Delay(1000);

    // Continue with other operations
} In the above example, the statement pauses the execution of the method until the operation completes. This allows other code, such as other async tasks, to execute concurrently. By using tasks, you can of asynchronous programming in C#. await Task.Delay(1000) Task.Delay harness the power Tip 2: Handling Multiple Exceptions with async await . When working with async code in C#, handling exceptions can present some challenges due to the asynchronous nature of the code. In certain situations, multiple exceptions can occur when executing async code. It’s important to be able to handle and aggregate these exceptions effectively. To handle multiple exceptions, C# provides the class, which allows us to group and manage multiple exceptions as a single entity. Exception handling is an important aspect of writing any software application AggregateException When multiple exceptions occur in an async operation, the class collects these exceptions and allows us to access and handle them in a centralized manner. This can be particularly useful for scenarios where we need to process all exceptions generated during an async operation. To handle exceptions using the class, we can wrap the code that may throw exceptions in a try-catch block. Within the catch block, we can access the property of the to access each individual exception that occurred. AggregateException AggregateException InnerExceptions AggregateException Here’s an example of how to use the class to handle and aggregate exceptions in async code: AggregateException try
{
    await SomeAsyncOperation();
}
catch (AggregateException ex)
{
    foreach (var innerException in ex.InnerExceptions)
    {
        // Handle each individual exception
        Console.WriteLine($"Exception: {innerException.Message");
    }
} In the example above, represents an async operation that may throw one or more exceptions. By wrapping the statement in a try-catch block and catching an , we are able to access and handle each individual exception that occurred within the async operation. By properly handling and aggregating exceptions in async code using the class, we can ensure that our code can gracefully handle any unexpected errors that may occur during async operations. SomeAsyncOperation await AggregateException AggregateException Tip 3: Avoiding Deadlocks with async await Deadlocks can occur when two or more tasks are waiting for each other to complete indefinitely, resulting in a state where none of the tasks can proceed. Stop Blocking Your Async Calls! One of the most common causes of deadlocks in async methods includes using the or methods inside an async method, which blocks the main thread and can lead to deadlocks. The best advice for avoiding deadlocks with async code: Task.Result Task.Wait “Don’t block on async code” Stephen Cleary He points out in his article that this tip (stop blocking) or the tip that immediately follows this (configuring the awaiter) are both able to help prevent deadlocks… but relying ONLY on the next pattern is more of a risk. That is, of these two suggestions, making your C# code async await all of the way through is the safer bet. That means instead of having something like: public class MyController(
  IWeatherService _weatherService) :
  ApiController
{
  public string GetWeatherData()
  {
    var weatherData = _weatherService.GetWeatherData(...).Result;
    return Results.OK(weatherData);
  }
} Instead, we’d make the top-level async as well: public class MyController(
  IWeatherService _weatherService) :
  ApiController
{
  public async Task<string> GetWeatherData()
  {
    var weatherData = await _weatherService.GetWeatherData(...);
    return Results.OK(weatherData);
  }
} As soon as we introduce a or call, we are effectively blocking — and that is one of the key situations that can lead to deadlocks as the application gets more complex. .Result .Wait() Using ConfigureAwait(false) with Async Await in C# To prevent deadlocks, the method can be used with the parameter. This instructs the operator to not capture the current synchronization context, allowing the continuation to be executed on any available thread from the thread pool — not just the thread that started the call! ConfigureAwait false await With , we can explicitly state that we do not require the original synchronization context to be captured, preventing the deadlock issue. Keep in mind that using should be done carefully and only when it is absolutely necessary, as it may introduce potential issues if the code relies on the captured synchronization context for specific requirements. For example, if you’re running UI logic and you come back to execution not on the UI thread, you may be in for some surprises! ConfigureAwait(false) ConfigureAwait(false) Let’s take a look at an example to illustrate the usage of to prevent deadlocks: ConfigureAwait(false) public async Task<string> GetDataAsync()
{
    // NOTE: assume we enter this method on some given thread

    // use a false parameter on ConfigureAwait
    await SomeAsyncMethod().ConfigureAwait(false);

    // NOTE: we could be on a different thread than when we started this method!

    return ProcessData();
} In the above example, is awaited using . This allows the continuation of the code to execute on any available thread, ensuring that the synchronization context is not captured and preventing potential deadlocks. SomeAsyncMethod ConfigureAwait(false) And while Stephen Cleary does say that this will help prevent deadlocks, he also had this to add: Using to avoid deadlocks is a dangerous practice. You would have to use for in the transitive closure of all methods called by the blocking code, . Using to avoid deadlock is at best ). ConfigureAwait(false) ConfigureAwait(false) every await including all third- and second-party code ConfigureAwait(false) just a hack Stephen Cleary Wrapping up Async Await in C# In this article, I’ve discussed 3 beginner tips for using async await in C# to write concurrent code. Async await in C# can be a lot to absorb right in the beginning, so it will take some time and practice for it to feel more natural! We got to see the basics of the keyword usage for async await — With this, you can now write asynchronous code! From there, we looked at how multiple exceptions can arise in async await code paths and how to navigate that. And we concluded with some ideas around preventing deadlocks by looking at 2 possibilities we can leverage. Async await in C# is something you’re going to want to learn and practice! If you found this useful and you’re looking for more learning opportunities, consider and check out my ! subscribing to my free weekly software engineering newsletter free videos on YouTube Also published here.

3 Beginner Tips You Need to Know for async await in C#

This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. Authors: (1) Dihia LANASRI, OMDENA, New York, USA; (2) Juan OLANO, OMDENA, New York, USA; (3) Sifal KLIOUI, OMDENA, New York, USA; (4) Sin Liang Lee, OMDENA, New York, USA; (5) Lamia SEKKAI, OMDENA, New York, USA. Table of Links Abstract & Introduction Background Related Work Our Methodology Experiments and Results Conclusion, Acknowledgments & References 2 Background Hate speech is commonly defined as any communication that disparages a target group of people based on some characteristic such as race, color, ethnicity, gender, sexual orientation, nationality, religion, or other characteristic De Gibert et al. [2018]. 2.1 Hate speech According to Al-Hassan and Al-Dossari [2019] hate speech is categorized into five categories: (1) gendered hate speech, including any form of misogyny and sexism; (2) religious hate speech including any religious discrimination, such as Islamic sects, anti-Christian, etc.; (3) racist hate speech including any racial offense or tribalism, and xenophobia; (4) disability including any sort of offense to an individual suffering from health problems; and (5) political hate speech can refer to any abuse and offense against politicians Guellil et al. [2022]. 2.2 Algerian Dialect and Arabic Languages Arabic is the official language of 25 countries[2] . More than 400 million people around the world speak this language. Arabic is also recognized as the 4th most-used language on the Internet Boudad et al. [2018]. Arabic is classified into three categories Habash [2022]: (1) Classical Arabic (CA), which is the form of the Arabic language used in literary texts. The Quran is considered the highest form of CA text Sharaf and Atwell [2012]. (2) Modern Standard Arabic (MSA) is used for writing and formal conversations. (3) Dialectal Arabic is used in daily life communication, informal exchanges, etc. Boudad et al. [2018] like the Algerian dialect, Tunisian dialect, etc. [2] https://worldpopulationreview.com/country-rankings/arabic-speaking-countries

Hate Speech Detection in Algerian Dialect Using Deep Learning: Background

This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. Authors: (1) Dihia LANASRI, OMDENA, New York, USA; (2) Juan OLANO, OMDENA, New York, USA; (3) Sifal KLIOUI, OMDENA, New York, USA; (4) Sin Liang Lee, OMDENA, New York, USA; (5) Lamia SEKKAI, OMDENA, New York, USA. Table of Links Abstract & Introduction Background Related Work Our Methodology Experiments and Results Conclusion, Acknowledgments & References 6 Conclusion The importance of hate speech detection on social networks has encouraged many researchers to build solutions (corpora and classifiers) to detect suspect messages. The literature review shows that most works are interested in text in structured languages like English, French, Arabic, etc. However, few works deal with dialects, mainly the Algerian one, which is known for its complexity and variety. To fill in the gap, we propose in this paper a complete NLP approach to detect hate speech in the Algerian dialect. We built an annotated corpus of more than 13,5K documents, which is used to evaluate various deep learning architectures. The obtained results are very promising, where the most accurate was the DzaraShield . Looking ahead, there is significant potential to enhance inference speed, particularly for the Dziribert-based and multilingual models. While this project primarily focused on Arabic characters, our next step will be to address the dialect when written in Latin characters. Embracing both Arabic and Latin characters will more accurately capture the nuances of the written Algerian dialect. Finally, we plan to expand our corpus size and explore alternative deep-learning architectures. 7 Acknowledgments We would like to thank every person who has contributed to this project: Micha Freidin, Viktor Ivanenko, Piyush Aaryan, Yassine Elboustani, Tasneem Elyamany, Cephars Bonacci, Nolan Wang and Lydia Khelifa Chibout. We would also like to thank Omdena organization for giving us this valuable opportunity References Sean MacAvaney, Hao-Ren Yao, Eugene Yang, Katina Russell, Nazli Goharian, and Ophir Frieder. Hate speech detection: Challenges and solutions. PloS one, 14(8):e0221152, 2019. Mirela P Bogdani, Federico Faloppa, and Xheni Karaj. Beyond definitions. a call for action against hate speech in albania. a comprehensive study november 2021. 2021. JT Nockleby. hate speech in encyclopedia of the american constitution. electronic journal of academic and special librarianship. 2000. Imane Guellil, Ahsan Adeel, Faical Azouaou, Mohamed Boubred, Yousra Houichi, and Akram Abdelhaq Moumna. Ara-women-hate: An annotated corpus dedicated to hate speech detection against women in the arabic community. In Proceedings of the Workshop on Dataset Creation for Lower-Resourced Languages within the 13th Language Resources and Evaluation Conference, pages 68–75, 2022. Thomas Davidson, Dana Warmsley, Michael Macy, and Ingmar Weber. Automated hate speech detection and the problem of offensive language. In Proceedings of the international AAAI conference on web and social media, volume 11, pages 512–515, 2017. Paula Fortuna and Sérgio Nunes. A survey on automatic detection of hate speech in text. ACM Computing Surveys (CSUR), 51(4):1–30, 2018. Anna Schmidt and Michael Wiegand. A survey on hate speech detection using natural language processing. In Proceedings of the fifth international workshop on natural language processing for social media, pages 1–10, 2017. Delphine Battistelli, Cyril Bruneau, and Valentina Dragos. Building a formal model for hate detection in french corpora. Procedia Computer Science, 176:2358–2365, 2020. Fatimah Alkomah and Xiaogang Ma. A literature review of textual hate speech detection methods and datasets. Information, 13(6):273, 2022. Flor Miriam Plaza-del Arco, M Dolores Molina-González, L Alfonso Urena-López, and M Teresa Martín-Valdivia. Comparing pre-trained language models for spanish hate speech detection. Expert Systems with Applications, 166: 114120, 2021. Nuha Albadi, Maram Kurdi, and Shivakant Mishra. Are they our brothers? analysis and detection of religious hate speech in the arabic twittersphere. In 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pages 69–76. IEEE, 2018. Hala Mulki, Hatem Haddad, Chedi Bechikh Ali, and Halima Alshabani. L-hsab: A levantine twitter dataset for hate speech and abusive language. In Proceedings of the third workshop on abusive language online, pages 111–118, 2019. Fréha Mezzoudj, Mourad Loukam, and Fatma Zohra Belkredim. Arabic algerian oranee dialectal language modelling oriented topic. International Journal of Informatics and Applied Mathematics, 2(2):1–14, 2019. Oussama Boucherit and Kheireddine Abainia. Offensive language detection in under-resourced algerian dialectal arabic language. arXiv preprint arXiv:2203.10024, 2022. Djamila Menifi, Wiam Moussa, and Ahmed Cherif Mazari. Transfer Learning and Deep Learning for Multilingual Algerian Dialect Hate Speech Detection. PhD thesis, 2022. Adel Abdelli, Fayçal Guerrouf, Okba Tibermacine, and Belkacem Abdelli. Sentiment analysis of arabic algerian dialect using a supervised method. In 2019 International Conference on Intelligent Systems and Advanced Computing Sciences (ISACS), pages 1–6. IEEE, 2019. Imane Guellil, Ahsan Adeel, Faical Azouaou, Mohamed Boubred, Yousra Houichi, and Akram Abdelhaq Moumna. Sexism detection: The first corpus in algerian dialect with a code-switching in arabic/french and english. arXiv preprint arXiv:2104.01443, 2021. Ona De Gibert, Naiara Perez, Aitor García-Pablos, and Montse Cuadros. Hate speech dataset from a white supremacy forum. arXiv preprint arXiv:1809.04444, 2018. Areej Al-Hassan and Hmood Al-Dossari. Detection of hate speech in social networks: a survey on multilingual corpus. In 6th international conference on computer science and information technology, volume 10, pages 10–5121, 2019. Naaima Boudad, Rdouan Faizi, Rachid Oulad Haj Thami, and Raddouane Chiheb. Sentiment analysis in arabic: A review of the literature. Ain Shams Engineering Journal, 9(4):2479–2490, 2018. Nizar Y Habash. Introduction to Arabic natural language processing. Springer Nature, 2022. Abdul-Baquee M Sharaf and Eric Atwell. Qurana: Corpus of the quran annotated with pronominal anaphora. In Lrec, pages 130–137, 2012. Kheireddine Abainia, Kenza Kara, and Tassadit Hamouni. A new corpus and lexicon for offensive tamazight language detection. In Proceedings of the 7th International Workshop on Social Media World Sensors, pages 1–6, 2022. Ahmed Cherif Mazari and Hamza Kheddar. Deep learning-based analysis of algerian dialect dataset targeted hate speech, offensive language and cyberbullying. International Journal of Computing and Digital Systems, 2023. Imane Guellil, Ahsan Adeel, Faical Azouaou, Sara Chennoufi, Hanene Maafi, and Thinhinane Hamitouche. Detecting hate speech against politicians in arabic community on social media. International Journal of Web Information Systems, 16(3):295–313, 2020. Djamila Mohdeb, Meriem Laifa, Fayssal Zerargui, and Omar Benzaoui. Evaluating transfer learning approach for detecting arabic anti-refugee/migrant speech on social media. Aslib Journal of Information Management, 74(6): 1070–1088, 2022. Reem ALBayari and Sherief Abdallah. Instagram-based benchmark dataset for cyberbullying detection in arabic text. Data, 7(7):83, 2022. Monirah A Al-Ajlan and Mourad Ykhlef. Optimized twitter cyberbullying detection based on deep learning. In 2018 21st Saudi Computer Society National Computer Conference (NCC), pages 1–5. IEEE, 2018. Batoul Haidar, Maroun Chamoun, and Ahmed Serhrouchni. Arabic cyberbullying detection: Enhancing performance by using ensemble machine learning. In 2019 international conference on internet of things (ithings) and ieee green computing and communications (greencom) and ieee cyber, physical and social computing (cpscom) and ieee smart data (smartdata), pages 323–327. IEEE, 2019. Hamdy Mubarak, Kareem Darwish, and Walid Magdy. Abusive language detection on arabic social media. In Proceedings of the first workshop on abusive language online, pages 52–56, 2017. Azalden Alakrot, Liam Murray, and Nikola S Nikolov. Dataset construction for the detection of anti-social behaviour in online communication in arabic. Procedia Computer Science, 142:174–181, 2018. Hatem Haddad, Hala Mulki, and Asma Oueslati. T-hsab: A tunisian hate speech and abusive dataset. In International conference on Arabic language processing, pages 251–263. Springer, 2019. Hamdy Mubarak, Ammar Rashed, Kareem Darwish, Younes Samih, and Ahmed Abdelali. Arabic offensive language on twitter: Analysis and experiments. arXiv preprint arXiv:2004.02192, 2020. Hala Mulki and Bilal Ghanem. Let-mi: an arabic levantine twitter dataset for misogynistic language. arXiv preprint arXiv:2103.10195, 2021. Rehab Duwairi, Amena Hayajneh, and Muhannad Quwaider. A deep learning framework for automatic detection of hate speech embedded in arabic tweets. Arabian Journal for Science and Engineering, 46:4001–4014, 2021. Wassen Aldjanabi, Abdelghani Dahou, Mohammed AA Al-qaness, Mohamed Abd Elaziz, Ahmed Mohamed Helmi, and Robertas Damaševicius. Arabic offensive and hate speech detection using a cross-corpora multi-task learning ˇ model. In Informatics, volume 8, page 69. MDPI, 2021. Batoul Haidar, Maroun Chamoun, and Ahmed Serhrouchni. A multilingual system for cyberbullying detection: Arabic content detection using machine learning. Advances in Science, Technology and Engineering Systems Journal, 2(6): 275–284, 2017. Ahmed Abdelali, Kareem Darwish, Nadir Durrani, and Hamdy Mubarak. Farasa: A fast and furious segmenter for arabic. In Proceedings of the 2016 conference of the North American chapter of the association for computational linguistics: Demonstrations, pages 11–16, 2016. Amine Abdaoui, Mohamed Berrimi, Mourad Oussalah, and Abdelouahab Moussaoui. Dziribert: a pre-trained language model for the algerian dialect. arXiv preprint arXiv:2109.12346, 2021. Sourab Mangrulkar, Sylvain Gugger, Lysandre Debut, Younes Belkada, and Sayak Paul. Peft: State-of-the-art parameterefficient fine-tuning methods. https://github.com/huggingface/peft, 2022. Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685, 2021. Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, and Furu Wei. 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Hate Speech Detection in Algerian Dialect Using Deep Learning: Conclusion,  Acknowledgments

USA v. eBay Court Filing, retrieved on January 11, 2024, is part of . You can jump to any part in this filing . This is part 7 of 12. HackerNoon’s Legal PDF Series here Actions in Furtherance of the Harassment and Intimidation Campaign 45. From on or about August 5, 2019 through at least September 6, 2019, the Individual Defendants took the following steps, among others, as part of the harassment and intimidation campaign: a. On or about August 6, 2019, Baugh, Gilbert, Cooke, and Popp met at eBay headquarters to plan the White Knight Strategy. b. On or about August 6, 2019, Baugh, Popp, Stockwell, Zea, and other members of the GIC met at eBay headqua1ters to plan the delivery to the Steiners' home of unwanted and disturbing items to distract the Steiners from publishing the Newsletter. c. On or about August 6, 2019, Stockwell purchased a laptop computer at a Best Buy store in San Jose, California for use in the harassment and intimidation campaign. d. On or about August 6, 2019, Popp created a Twitter account in the name of "@Tui_ Elei" that used a picture of a skeleton mask as a profile picture. e. On or about August 7, 2019, Popp sent the following DMs from the @Tui_Elei Twitter account to Ina Steiner's Twitter account: • "[Ina Steiner's First Name] ...whats your problem w/ebay? You know that's how we pay rent." • "HELLO!!!!!!! g. On or about August 9, 2019, Popp sent the following DMs from the@Tui_Elei Twitter account to Ina Steiner's Twitter account: • "WTF ...whats it goin to take for u to answer me??" • "I guess im goin to have to get ur attention another way bitch ..." • "U don't have the balls to talk to me?? Stop hiding behind ur computer screen u fuckin cunt!!!" h. On or about August 9, 2019, Stockwell used an anonymous email account to order live spiders and fly larvae for delivery to the Steiners' home. i. On or about August 9, 2019, one or more of the individual Defendants ordered a subscription for pornographic magazines in the name of David Steiner to be sent to the Steiners' neighbors. J. On or about August 9, 2019, one or more of the Individual Defendants attempted to order a pig fetus for delivery to the Steiners' home. k. On or about August 10, 2019, one or more of the Individual Defendants ordered a Halloween Pig Mask for delivery to the Steiners' home. I. On or about August 10, 2019, Popp sent the following DM from the@Tui_Elei Twitter account to Ina Steiner's Twitter account: • "DO I HAVE UR ATTENTION NOW????" m. On or about August 10, 2019, Popp sent the following DMs from the @Tui_Elei Twitter account to Ina Steiner's Twitter account: • "Ur fat fuck pussy husband [David Steiner's first name] needs to put u in Iine cunt." • "after he takes the plugs out of his asshole ...fuckin pussies!!!" n. On or about August 11, 2019, Baugh directed Harville to travel with him to Boston for an "op" targeting Ina Steiner and her website (i.e., EcommerceBytes). o. On or about August 11, 2019, Baugh sent Harville the following text message: "I won't send the bosses texts, but I've been ordered to find and destroy." Harville replied, "Copy. Totally black[.] I'm deleting this now." p. On or about August 12, 2019, Baugh sent Popp, Stockwell, Zea, and other GIC analysts the following WhatsApp message: "Starting now through Tuesday night double our effort on everything. Spam, house deliveries, etc. I don't want anything delivered on Thursday, so the cutoff should be Wednesday night - wake them up with a limo driver or something and then everything goes cold Thursday morning.... Take down all Craigslist posts late Wednesday night. Stop spam late Wednesday night, etc." q. On or about August 12, 2019, one or more of the Individual Defendants sent a book titled "Grief Diaries: Surviving Loss of a Spouse" to the Steiners' home. r. On or about August 12, 2019, Popp used the @Tui_Elei Twitter account to post publicly to Ina Steiner's Twitter account: "[Ina Steiner] ...wats ur problem with ebay??" s. On or about August 12, 2019, Popp used the @Tui_Elei Twitter account to post publicly to Ina Steiner's Twitter account: "many familys including mine make money 2 pay 4 food cloths and rent by selling on ebay...UR stupid idkiot comments r pushin buyers away from ebay and hurtin familys! ! ! STOP IT NOW!!" t. On or about August 13, 2019, one or more of the Individual Defendants used a prepaid debit card to order a funeral wreath for delivery to the Steiners' home. u. On or about August 14, 2019, one or more of the Individual Defendants ordered live cockroaches for delivery to the Steiners' home. v. On or about August 14, 2019, Popp used the @Tui_Elei Twitter account to post a public message to Ina Steiner's Twitter account: "[First name of Ina Steiner] wen u hutt our bizness u huit our familiys ...Pp! will do ANYTHING 2 protect family!!!!" w. On or about August 14, 2019, Baugh, Harville, Zea, Gilbert, and Popp met to discuss a trip to Natick, Massachusetts to install a GPS tracking device on the Steiners' car and to surveil the Steiners. At the meeting, Baugh instructed Zea to stop harassing deliveries that would interfere with the surveillance operation. x. On or about August 14, 2019, Zea attempted to register herself and Harville for a software development conference in Boston as false cover for the surveillance trip. y. On or about August 15, 2019, Harville used his eBay-issued phone to visit a website that could be used to monitor the "Natick Police and Fire Live Audio Feed." z. On or about August 15, 2019, Baugh, Harville, and Zea flew from California to Boston, Massachusetts. aa. On or about August 15, 2019, Stockwell sent a text message to Harville providing the license plate numbers for the Steiners' cars. bb. On or about August 15, 2019, Baugh, Harvi Ile, and Zea drove to the Steiners' home in Natick and attempted unsuccessfully to install a GPS tracking device on the Steiners' car, which was locked in the Steiners' garage. cc. On or about August 15, 2019, Baugh, Harville, Popp, and Zea dialed in to a conference line to communicate during the surveillance operation and to monitor the NPD dispatch. dd. On or about August 15, 2019, Baugh directed Stockwell by WhatsApp message to prepare an eBay "Person of lnterest" report ("POI Report") that listed the Steiners as eBay's top POis. Baugh wrote, "In the narrative I need you to write that they have made direct threats to ebay, [eBay's CEO], and our employees (make it up)." ee. On or about August 16, 2019, Harville purchased tools at a Boston hardware store for the purpose of breaking into the Steiners' locked garage. ff. On or about August 16, 2019, Baugh, Harville, and Zea returned to Natick in a rented Dodge Caravan to surveil the Steiners, and drove past their home repeatedly. gg. On or about August 16, 2019, Harville dialed in to the surveillance team's conference line. hh. On or about August 16, 20I 9, Baugh, Harville, and Zea followed David Steiner as he drove around Natick. ii. On or about August 16, 2019, believing that David Steiner had spotted the surveillance, Baugh returned the Dodge Caravan to the rental car agency. jj. On or about August 16, 2019, after the failed surveillance, Baugh directed that harassing deliveries to the Steiners resume. kk. On or about August 17, 2019, at 3:08 a.m. (EDT), Stockwell researched Bostonarea 24-hour drain repair services on the internet for the purpose of sending a repairman to the Steiners' home in the middle of the night. II. On or about August 17, 2019, one or more of the Individual Defendants ordered pizza to be delivered to the Steiners' home at 4:30 a.m., for payment upon delivery. mm. On or about August 17, 2019, Harville returned to California, and Popp traveled to Boston to replace him on the surveillance team. nn. On or about August 18, 2019, Popp replied to a public tweet on Ina Steiner's Twitter account using the @Tui_Elei account and stated: "Dis ur address???" The message was followed by information about Ina Steiner, accurately stating her name, age, address, and telephone number. oo. On or about August 18, 2019, Popp sent the following DM from the @Tui_Elei Twitter account to Ina Steiner's Twitter account: "U get my gifts cunt!!??" pp. On or about August 18, 2019, one or more of the Individual Defendants posted an advertisement on Craigslist claiming to be a married couple seeking a sexual partner or partners, and providing the Steiners' home address. qq. On or about August 18, 2019, Baugh, Popp, and Zea followed David Steiner as he drove in Natick. rr. On or about August 20, 2019, Baugh sent a text message to Popp, Gilbert, and Cooke that the Individual Defendants had "burned" two surveillance cars and that either the Steiners or the NPD were "seeing ghosts right now." ss. On or about August 20, 2019, Baugh forwarded to Gilbert the POI Report falsely indicating that the Steiners were security threats to eBay. In a WhatsApp message to Gilbert, Cooke, and Popp, Baugh stated: "Just sent poi doc with [the Steiners] included. I had GIC send this to me last week in case we got stopped .. that way would at least have something to show to PD." tt. On or about August 20, 2019, after Twitter suspended the @Tui_Elei account for posting the Steiners' home address, Popp created a new Twitter account, @Elei_ Tui, for use in harassing the Steiners. uu. On or about August 20, 2019, Popp also created two other Twitter accounts, including one that used the name ofa prominent eBay seller. vv. On or about August 21, 2019, Zea and Baugh made false statements to an NPD detective who came to the surveillance team's hotel to investigate Zea and Harville's connection to the cyberstalking campaign. ww. On or about August 21, 2019, at approximately 9:36 a.m. (EDT), Baugh sent the following text message to Popp, Gilbe1t, and Cooke: "Natick detective is in lobby looking for [Zea]. I've taken her away from hotel headed to airport...Detective called her cell. I answered just now as her husband and played dumb." xx. On or about August 21, 2019, at approximately 9:39 a.m. (EDT), Gilbert proposed to Baugh, Cooke, and Popp bringing "dossiers" on the Steiners to the NPD: "Definitely want to make them look crazy." yy. On or about August 21, 2019, between approximately 10:30 a.m. and 12:00 p.m. (EDT), Popp sent more threatening tweets to and about Ina Steiner and the Newsletter ("the Concerning Tweets"). Popp used the three Twitter accounts that she had created the night before to have a public "conversation" on Twitter, asking over one account what Ina Steiner's address was, and responding from the other "guest I hav to pay Ina a visit." zz. On or about August 21, 2019, minutes after writing the Concerning Tweets, Popp forwarded images of the Concerning Tweets to Baugh and Gilbert, writing falsely that Ina Steiner was "really bringing out some angry Twitter users." aaa. On or about August 21, 2019, at approximately 12:34 p.m. (EDT), Baugh forwarded the Concerning Tweets to Harville. bbb. On or about August 21, 2019, at approximately 2:30 p.m. (EDT), executing the White Knight Strategy, Gilbert called the Steiners, identified himself as an eBay employee, and offered eBay's assistance. ccc. After the call, Gilbert messaged Baugh, Popp, and Cooke that the Steiners were "totally rattled." ddd. Thereafter, still on August 21, 2019, Gilbert spoke, text messaged, and emailed with an NPD detective who was handling the investigation into the harassment of the Steiners. Gilbert falsely claimed not to know Zea or Harville and falsely stated that he had to travel to Toronto and New York but would instead attempt to come to Boston to meet with the NPD. eee. On or about August 21, 2019, after learning that the NPD was investigating the use of a prepaid debit card to purchase pizzas for delivery to the Steiners, Baugh and Popp directed Stockwell to prepare a list of eBay "Persons of lnterest" in the San Francisco Bay Area that could be used (as part of Gilbert's "dossier") to deflect attention from Zea. fff. On or about August 21, 2019, Stockwell created the document that Baugh requested-"Bay Area POis_August 2019.docx"- and emailed it to Popp and Baugh, who forwarded it to Gilbert for use in the NPD meeting. ggg. On or about August 22, 2019, Gilbert and another eBay employee met with three NPD officers at the NPD. During the meeting, Gilbert falsely stated that: • Zea and Harville had come to Boston to attend a conference; • Popp was Zea's supervisor; and • Popp had assigned Zea to a Person of Interest investigation regarding the Steiners, and that Zea had driven to Natick "on her own." hhh. On or about August 22, 2019, after the NPD meeting, Popp used one of the new Twitter accounts to post "@[Newsletter] 20 yrs of lies n distroin familys ... dunt b proud of dat u wurthless BITCH!!! i will distroy ur family n bizness 2… C how u like it…\n\n@Elei_Tui wen r we goin 2 visit her in natik???” jjj. On or about August 23, 20I 9, Baugh falsely told eBay investigators his team was not responsible for harassing messages or deliveries and that his team had been in Natick to investigate threats to the Steiners. kkk. On August 23, 2019, at 5:15 p.m. (PDT), Baugh messaged Executive 2 as follows: Hi [Executive 2] - this is Jim Baugh's personal cell. My team ran an Op on our friend in Boston. Nothing illegal occurred and we were actually intending to team up with her and get her on our side in a positive manner. However, small town police got a couple of rental car plates and tracked it back to my people and the hotel the were staying at. They sent a note to ebay investigations GAP team who then passed it to legal and they are conducting an internal investigation on us. We are cooperating, but I know they realize something is off. We will continue to cooperate, but not sure how much longer we can keep this up. If there is any way to get some top cover that would be great. If not, I just wanted you to have a heads up because they are aware that multiple members of the ELT are not a fan of that website to include [redacted] and his wife. Again, no crime was committed and local police don't have a case. I don't want our legal team to give them one. Let me know if you want to discuss this weekend. lll. On or about August 26, 2019, Harville falsely told eBay investigators that he didn't know whether Zea had gone to Natick and that he had not worked on a matter involving the Steiners. mmm. On or after August 26, 2019, Baugh, Harville, Popp, Zea, Gilbett, and others deleted and attempted to delete data from their mobile phones that evidenced the conspiracy, including WhatsApp messages. nnn. On or about August 27, 2019, Harville falsely told eBay investigators that he had attended a conference in Boston with Zea. ooo. On or about August 28, 2019, Baugh falsely told eBay investigators that his team was not responsible for the deliveries or harassing communications. ppp. On or about August 30, 2019, Baugh directed an eBay employee to retrieve computers from the GIC and to bring them to his house. qqq. On or about August 30, 2019, after receiving an email from an eBay investigator directing him to preserve relevant information and to turn in his eBay-issued cell phone, Harville messaged Baugh, Gilbert, and Popp, "Want me to wipe it." rrr. On or about August 30, 2019, Harville turned in his eBay-issued cell phone from which significant data related to his trip to Natick had been deleted. sss. On or about September 6, 2019, Popp posted five new Tweets to one ofthe Twitter accounts that the Individual Defendants had used to harass the Steiners, to suggest that those responsible for harassing the Steiners were still at large. Continue Reading . Here About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case retrieved on January 11, 2024, from is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. ecommercebytes.com

How High-Ranking eBay Executives Tried to Silence Critics

USA v. eBay Court Filing, retrieved on January 11, 2024, is part of . You can jump to any part in this filing . This is part 6 of 12. HackerNoon’s Legal PDF Series here The Harassment and Intimidation Campaign 28. On or about August 1, 2019, the New York Times reported that eBay had accused Amazon in a lawsuit of unlawfully poaching eBay sellers to Amazon's online marketplace. 29. That day, at approximately 1 :46 p.m. (EDT), Ina Steiner posted an article on EcommerceBytes under the headline, "eBay RJCO Lawsuit Meant to Curb Seller Exodus to Amazon?" She wrote: "[Executive] 1 has been unable to stop a decline in market sales, but trying to dissuade sellers from turning to Amazon (and trying to get Amazon to stop recruiting sellers) may not be the best tactic." 30. At approximately 2: 19 p.m. (EDT), Executive 1 texted Executive 2: "Ina is out with a hot piece on the litigation. If we are ever going to take her down ..now is the time." 31. Executive 2 responded at approximately 2:36 p.m. (EDT): "On it." 32. As set forth below, a series of text messages followed between Executive 2 and Baugh, beginning with Executive 2's passing along of Executive l's suggestion that "we ... take her down": 33. Executive 2 and Baugh's August 1, 2019 conversation continued by text, with Baugh committing to put in motion a "plan B" and Executive 2 committing to "embrace managing any bad fall out." 34. Baugh continued, stating falsely that Fidomaster-whom he refe1nd to as the "unsuck idiot"-was David Steiner "or [an]other close associate" off Ina Steiner's: 35. Baugh concluded the August 1 exchange with Executive 2 as follows: 36. On or about August 6, 2019, an eBay seller contacted Executive 1 to complain about @unsuckebay, one of the Twitter accounts controlled by Fidomaster. The seller's complaint led Executive 2, Executive 1, Baugh, and eBay's general counsel to communicate about eBay's unsuccessful effo1ts to have the Twitter account shut down. 37. Executive 2's email concluded: "I am utterly vexed by this! This twitter account [Fidomaster] dominates our social narrative with his CONSTANT obsession with trolling us. It's more than annoying, it's very damaging." 38. The email noted that Fidomaster and "the eCom[m]ercebytes gal"-a reference to Ina Steiner- were "infatuated with eBay" and "have seemingly dedicated their Jives to erroneously trashing us as a way to build their own brand-or even build a business." It continued, "This issue gives me ulcers, harms employee moral, and trickles into everything about our brand.  I genuinely believe these people are acting out of malice and ANYTHING we can do to solve it should be explored. Somewhere, at some point, someone chose to let this slide. It has grown to a point that is absolutely unacceptable. It's the 'blind eye toward graffiti that turns into mayhem' syndrome and I'm sick about it. Whatever. It. Takes." 39. On or about August 7, 2019, at 4:59 p.m. (PDT), Baugh asked Executive 2 by message, "lf l can neutralize Ina's website in two weeks or less, does that work for you?" Within minutes, Executive 2 responded: "I want to see ashes. As long as it takes. Whatever it takes." 40. After receiving Executive 2's text messages and emails about the Steiners, EcommerceBytes, and Fidomaster, Baugh shared them with, among others, Harville, Gilbert, Cooke, Popp, Stockwell, and Zea. 41. Beginning on or about August 5, 2019 and continuing through at least September 6, 2019, Baugh, Harville, Gilbert, Cooke, Popp, Stockwell, and Zea (together "the Individual Defendants") worked together to harass and intimidate the Steiners, and to place them under surveillance with.the intent to harass and intimidate them, through repeated and hostile Twitter messages, deliveries of unwanted-and in some instances disturbing-items to the Steiners' home, and travel to Massachusetts to conduct physical surveillance. The Individual Defendants' conduct caused, attempted to cause, and would reasonably have been expected to cause substantial emotional distress to the Steiners. 42. The purposes of the harassment campaign were, among other things, to distract the Steiners from publishing EcommerceBytes, to alter the website's coverage of eBay, and to gather information that the Individual Defendants could use to discredit the Steiners and EcomrnerceBytes. 43. The Individual Defendants took steps to conceal their harassment campaign from eBay investigators and state and federal authorities, by, among other things, using non-eBay electronic communications platforms, billing expenses related to the campaign to an outside contractor, monitoring law enforcement communications, forging records, lying to investigators, and destroying evidence. 44. Among the manner and means by which Individual Defendants carried out the harassment campaign were the following: a. Creating Twitter accounts in false names that featured ominous profile photos. b. Using these Twitter accounts to send threatening private direct messages ("DMs") to Ina Steiner about her, David Steiner, and EcommerceBytes. c. Publicly posting the Steiners' home address on Twitter, while suggesting in threatening messages that eBay sellers who were angry about EcommcerceBytes' coverage were going to visit the Steiners' home. d. Ordering unwanted and scary items and services to the Steiners' home, and ordering items intended to embarrass the Steiners to their neighbors' addresses. e. Posting online advertisements for fictitious events at the Steiners' home, including sexual encounters, to encourage strangers to visit them there. f. Traveling to Natick to install a GPS device on the Steiners' car, and to surveil the Steiners in their home and in their community. g. Monitoring law enforcement communications to avoid detection. h. Establishing false cover stories for the surveillance trip, including that the coconspirators were investigating the Steiners for threatening Executive 1. i. Continuing surveillance, even after the Steiners had detected it, with the purpose of intimidating them. J. Disguising their roles in the conspiracy by harassing and intimidating the Steiners using prepaid cell phones and laptops, VPN services, overseas email accounts, and prepaid debit cards purchased with cash. k. Offering the Steiners assistance with the very harassment they were committing, to earn the Steiners' good will ("the White Knight Strategy"). I. Making false and misleading statements to NPD personnel who were investigating the threatening communications, harassing deliveries, and surveillance of the Steiners. m. Communicating by WhatsApp regarding how to harass the Steiners and how to respond to the NPD investigation. n. Creating a "dossier" of their own threats to the Steiners, which they planned to show to the NPD as proof that Zea and Harville were in Massachusetts investigating the harassment of the Steiners (purportedly by third parties). o. Exchanging ideas about how best to thwart the NPD investigation, including creating false suspects, continuing the harassing deliveries, fabricating cover stories, and intervening with any San Jose area police that the NPD contacted to further its investigation. p. Lying to eBay investigators who were responding to NPD requests for eBay's assistance. q. Exchanging ideas about how best to thwart eBay's internal investigation. r. Deleting the contents of computers, cell phones, and social media accounts that evidenced the harassment and intimidation campaign and the defendants' and their coconspirators' efforts to obstruct the NPD investigation. Continue Reading . Here About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case retrieved on January 11, 2024, from is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. ecommercebytes.com

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