产品管理 (Product Management)

A couple of months ago, I decided to try something new. The MVP Lab by Mozilla is an 8-week incubator for pre-startup teams to explore product concepts and, over the 8 weeks of the program, ship a minimum viable product that people want to use. My team worked hard to come up with a product idea and submit an application for consideration. We didn’t make the cut; however, I learned many product ideations and management concepts along the way. Below is the product idea that I believe is worth sharing!

几个月前，我决定尝试一些新的东西。 Mozilla 的MVP Lab是一个为期8周的孵化器，供创业前团队探索产品概念，并在计划的8周内交付了人们希望使用的最低可行产品。 我的团队努力工作，提出了一个产品创意，并提交了一份申请供考虑。 我们没有晋级； 但是，在此过程中，我学到了许多产品理念和管理概念。 以下是我认为值得分享的产品创意！

产品创意描述：最低可行产品将在8周内达到什么水平？ (Description of product idea: What will the Minimum Viable Product be in 8 weeks?)

The “personalization” on the web or the algorithmically confounded choices given to us can lead to filter bubbles that often limit us from reading ideologically diverse ideas, opinions, and more recently, news. Media bias is a huge problem all around the world, but according to studies, media houses are often characterized into left leaning or right leaning without proper analysis. A fair and balanced political environment can be created when media houses are tracked for bias daily and people get to see all angles to the story by popping the bubble!

网络上的“个性化”或给我们带来的算法上的混淆选择，可能会导致过滤器气泡，这些气泡通常会限制我们阅读意识形态上不同的想法，观点以及最近的新闻。 媒体偏见是世界范围内的一个巨大问题，但是根据研究，媒体机构通常被归类为左倾或右倾，而没有进行适当的分析。 当每天跟踪媒体公司的偏见，并通过弹出气泡来了解故事的各个角度时，便可以创建一个公平，平衡的政治环境。

Minimum Viable Product in 8 weeks: A website and an app where users will be able to read anonymous articles and rate content (without having their own bias about media houses) and also see how the media bias changes over time in media outlets for a variety of issues. The intend is to not only become a credible source of media bias quantification like Snopes.com is for accuracy, but also to bring people and communities together for a less polarized, filter bubble-free world.

8周内的最低可行产品：一个网站和一个应用程序，用户可以在其中阅读匿名文章和对内容进行评分(而不必对媒体公司有自己的偏见)，还可以查看各种媒体在不同时间的偏见如何变化问题。 这样做的目的不仅是要像Snopes.com一样，成为可靠的媒体偏差量化来源，而且还要使人们和社区团结起来，形成一个两极分化，无气泡的世界。

Media bias is believed to operate via two mechanisms: selective coverage of issues, known as issue filtering, and how issues are presented, known as issue framing. Also, there is no agreed-upon methodology or source for quantifying media bias. The MVP will be based on a research paper “Fair and Balanced?” which uses a combination of machine learning and crowdsourcing techniques to effectively tackle this problem.

媒体偏见被认为是通过两种机制来运作的：对问题的选择性覆盖(称为问题过滤 )和如何呈现问题(称为问题框架) 。 同样，也没有商定的方法来量化媒体的偏见。 MVP将基于研究论文“ 公平与平衡？ ”结合了机器学习和众包技术来有效解决此问题。

To address the problem of quantifying issue framing, an NLP based classifier will be built that will be able to classify articles into “political” and “non-political” categories and further into subsets like news, opinion, and also issues such as healthcare, economy, etc. This classifier will be used to filter out non-political content from our website/app and also tag articles with issues or label them as news or opinion. For issue framing, content-based quantification methodologies are often preferred over audience-based ones. The website/app will track media bias on a daily basis through crowdsourced content analysis.

为了解决量化问题框架的问题，将构建一个基于NLP的分类器，该分类器将文章分类为“政治”和“非政治”类别，并进一步分类为新闻，观点以及医疗保健等问题的子集，经济等。此分类器将用于从我们的网站/应用中过滤掉非政治性内容，并标记有问题的文章或将其标记为新闻或观点。 对于问题框架，基于内容的量化方法通常比基于受众的方法更为可取。 该网站/应用将通过众包内容分析每天跟踪媒体的偏见。

A user entering PopTheBubble will be presented with anonymized political articles. After reading each anonymized article, the user will be asked to rate it on a scale of left-leaning to right-leaning. All such results will in turn be used to quantify slant in issue framing. This way, not only do slants of media houses get captured at an outlet level but also an issue level.

进入PopTheBubble的用户将看到匿名的政治文章。 阅读每篇匿名文章后，将要求用户按从左倾斜到右倾斜的等级对其进行评分。 所有这些结果将反过来用于量化问题框架中的偏差。 通过这种方式，不仅可以在出口级捕获媒体馆的倾斜，而且可以在发行级捕获。

People want unbiased news and are willing to explore newer platforms that provide it. Many people also prefer aggregation of various issue-based political news but news channels and even social media (through their “personalized” news feed) nowadays are rarely moderate; they write or promote articles either far right or far left which can provide a skewed outlook of reality. Hence, people will use PopTheBubble to check the polarity of media outlets and get ideologically differing news and opinions.

人们想要公正的新闻，并愿意探索提供新闻的新平台。 许多人也更喜欢汇总各种基于问题的政治新闻，但如今新闻渠道，甚至社交媒体(通过其“个性化”新闻源)很少适度。 他们撰写或宣传的文章可能偏右或偏左，这可能会提供歪曲的现实观。 因此，人们将使用PopTheBubble来检查媒体渠道的极性，并获得意识形态上不同的新闻和观点。

The above-discussed methodologies will allow the creation of a website/app that tracks bias/slant in real-time which no other website or app does right now. In the future, PopTheBubble can be extended for a variety of purposes including giving smaller media houses a platform to publish their articles, becoming a preprint bias checking tool for larger media houses who want results in real-time, and allowing people to express their opinions on the articles. The startup will be a Software as a Service (SaaS) for media houses and a product for the consumers.

以上讨论的方法将允许创建一个实时跟踪偏见/偏见的网站/应用程序，而其他任何网站或应用程序现在都没有。 将来，PopTheBubble可以扩展为多种用途，包括为较小的媒体公司提供发布文章的平台，成为希望实时获得结果的大型媒体公司的预印偏差检查工具，并允许人们表达自己的意见。在文章上。 该初创公司将是面向媒体公司的软件即服务(SaaS)和面向消费者的产品。

竞争对手： (Competitors:)

Very few third party companies exist that check and track the bias of big media companies. Even a thorough search found no big players in the market. Two sites that had similar functionalities were allsides.com and mediabiasfactcheck.com. While allsides.com classifies and presents facts from different perspectives, they only let voting/rating at a media outlet level (which isn’t fair) and there is no way to interact or vote for these articles. Also, they still show the news outlet which wrote the articles. Mediabiasfactcheck.com is pretty much static and doesn’t let users have a say. Although similar, these sites are way off from our goal. At present many news companies have an internal system for checking the biases of their content but no third party crowdsourced provider. PopTheBubble can be that in the long run.

很少有第三方公司能够检查和跟踪大型媒体公司的偏见。 即使进行彻底的搜索，市场上也没有大公司。 功能相似的两个站点是allsides.com和mediabiasfactcheck.com 。 尽管allsides.com从不同角度对事实进行分类和呈现，但它们只允许在媒体级别进行投票/评分(这是不公平的)，并且无法对这些文章进行互动或投票。 此外，他们仍然显示撰写文章的新闻媒体。 Mediabiasfactcheck.com几乎是静态的，不会让用户发表意见。 尽管类似，但这些网站与我们的目标相去甚远。 目前，许多新闻公司都有一个内部系统来检查其内容的偏差，但没有第三方众包提供者。 从长远来看，PopTheBubble可能就是这样。

用户获取： (User Acquisition:)

The first 1000 users will be attracted via our professional networks and social networks (LinkedIn, Facebook, Instagram, Twitter) and by writing articles on LinkedIn and Medium to make people aware of our platform. Once all the social media resources are used up, the option of online advertisements will be explored.

前1000名用户将通过我们的专业网络和社交网络(LinkedIn，Facebook，Instagram，Twitter)以及在LinkedIn和Medium上撰写文章来吸引人们，使他们意识到我们的平台。 一旦所有社交媒体资源用完，将探索在线广告的选项。

前两周的里程碑： (First two weeks milestone:)

A simple website which aggregates all the news and allows the user to rate it on a scale of left-leaning to right-leaning. The steps involved would be gathering the data through APIs and aggregating them and then automating it using AWS lambda. Meanwhile, two of our developers would start building a react backend for the website and one of the developers will learn react-native for the app. Also, some time will be spent on creating mockups and designing the system. Our NLP classifiers should have labeled data and a final model to train on by the end of two weeks.

一个简单的网站，可汇总所有新闻，并允许用户以从左倾到右倾的比例对其进行评分。 涉及的步骤将是通过API收集数据并将其聚合，然后使用AWS lambda将其自动化。 同时，我们的两名开发人员将开始为网站构建React后端，其中一名开发人员将为该应用程序学习react-native。 另外，将花费一些时间来创建模型和设计系统。 我们的NLP分类器应具有标记的数据和最终模型，以便在两周后进行训练。

技术细节： (Technical Details:)

As explained in the product idea, we’ll tackle the problem of quantifying and tracking media bias through content-based crowd-sourcing:

正如产品构想中所述，我们将通过基于内容的众包解决量化和跟踪媒体偏见的问题：

1. Popular US media websites (around 20) will be scraped on a daily or hourly basis. This can be done using general news APIs like Google News API, News API, Bing Search API, etc, or from specific media houses like News York Times API, BBC News API, etc.
   美国流行的媒体网站(约20个)将每天或每小时被删除。 可以使用通用新闻API(例如Google新闻API，新闻API，Bing搜索API等)来完成此操作，也可以使用特定的媒体机构(例如新闻纽约时报API，BBC新闻API等)来完成。

2. We pass it through a classifier that classifies it as political or not, news or opinion, and tags it with issues within the political landscape, etc. To build the classifier, we will need to train an NLP based model on labeled data. We intend to do this using already available datasets like News Category Dataset on Kaggle and using the data obtained from APIs from step 1 to create our own dataset. In case of creating our own labeled dataset, the labeling task will be crowdsourced on Amazon MTurk.

   我们通过分类器将其分类为政治或非政治，新闻或观点，并在政治领域内对问题进行标记等。要构建分类器，我们将需要在标记数据上训练基于NLP的模型。 我们打算使用已经可用的数据集(例如Kaggle上的“ 新闻类别数据集 ”)并使用从步骤1中从API获取的数据来创建我们自己的数据集。 如果创建自己的标记数据集，则标记任务将在Amazon MTurk上众包。

3. Anonymize the article and present it to users who’ll read it on our website and rate it to be left-leaning, right-leaning, etc (on a scale). The display strategy for our NewsFeed can be popularity based, issue-based, or just timestamp-based.
   对文章进行匿名处理，然后将其呈现给在我们的网站上阅读并评价为左倾，右倾等(在一定范围内)的用户。 我们NewsFeed的显示策略可以是基于流行度，基于问题或仅基于时间戳。
4. Use the classification results from step 2 and crowdsourced results from step 3 to quantify, display, and track media bias. This way we precisely quantify and display how bias in issue filtering and issue framing changes overtime for every media outlet overall, at news/opinion level, and an issue level.
   使用步骤2的分类结果和步骤3的众包结果来量化，显示和跟踪媒体偏见。 这样，我们可以精确地量化并显示问题筛选和问题框架中的偏见如何随新闻/意见级别和问题级别上的每个媒体整体随时间变化。

用这个想法可以预期的挑战： (Challenges to anticipate with this idea:)

One of the biggest issues that this product may face in the future is potential privacy/legal issues from news outlets and media companies. We are displaying articles anonymously coming from many big news companies and they would need some sort of accreditation or reference within the product. To tackle this, we plan to include a “View Publisher” toggle button that allows users to view a reference to the particular news outlet that generated the article that the user is looking at. Since viewing the reference of the article would result in bias, the option for the user to review the article would be disabled once this toggle button is pressed.

该产品将来可能面临的最大问题之一是新闻媒体和媒体公司的潜在隐私/法律问题。 我们正在匿名显示来自许多大型新闻公司的文章，这些文章需要产品中的某种认证或参考。 为了解决这个问题，我们计划包括一个“ View Publisher”切换按钮，该按钮允许用户查看对生成该用户正在查看的文章的特定新闻媒体的引用。 由于查看文章的参考文献会导致偏见，因此，一旦按下此切换按钮，用户就无法查看文章。

Our second challenge will be monetization. A monetization plan will be heavily beneficial to retrieve early-stage investors and bootstrap our finances. Through the development and deployment of our idea, the information and insights that we are collecting could be invaluable to media outlets. Through analytics and insights of what the consumer is inputting into the product, we can leverage information on specific articles and approach media outlets to potentially sell this information or act as the MTurk for them! Another convenient source of funding is through advertisements, either through Google Ads or other vendors. As our customer base grows (1000+ users), we can leverage our marketability and outreach by putting in ads into our product. The growth of our product will then correlate with the revenue that the product is bringing in.

我们的第二个挑战将是货币化。 货币化计划将极大地吸引早期投资者并引导我们的财务状况。 通过开发和部署我们的想法，我们正在收集的信息和见解对于媒体而言可能是无价的。 通过对消费者输入产品的分析和见解，我们可以利用特定文章上的信息，并与媒体联系以潜在地出售此信息或为他们充当MTurk！ 另一个方便的资金来源是通过广告(通过Google Ads或其他供应商)。 随着客户群的增长(1000多个用户)，我们可以通过在产品中投放广告来利用我们的可销售性和拓展性。 我们产品的增长将与产品带来的收入相关。

I’d love to hear your thoughts on the product idea. I’m new to the Product Management world and would be grateful for your feedback! You can find me on LinkedIn or comment below. Thank you for reading!

我很想听听您对产品创意的想法。 我是产品管理界的新手，非常感谢您的反馈！ 您可以在LinkedIn上找到我或在下面发表评论。 感谢您的阅读！