產品管理 (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!

幾個月前，我決定嘗試一些新的東西。 Mozilla 的MVP Lab是一個為期8周的孵化器，供創業前團隊探索產品概念，并在計劃的8周內交付了人們希望使用的最低可行產品。 我的團隊努力工作，提出了一個產品創意，并提交了一份申請供考慮。 我們沒有晉級； 但是，在此過程中，我學到了許多產品理念和管理概念。 以下是我認為值得分享的產品創意！

產品創意描述：最低可行產品將在8周內達到什么水平？ (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!

網絡上的“個性化”或給我們帶來的算法上的混淆選擇，可能會導致過濾器氣泡，這些氣泡通常會限制我們閱讀意識形態上不同的想法，觀點以及最近的新聞。 媒體偏見是世界范圍內的一個巨大問題，但是根據研究，媒體機構通常被歸類為左傾或右傾，而沒有進行適當的分析。 當每天跟蹤媒體公司的偏見，并通過彈出氣泡來了解故事的各個角度時，便可以創建一個公平，平衡的政治環境。

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周內的最低可行產品：一個網站和一個應用程序，用戶可以在其中閱讀匿名文章和對內容進行評分(而不必對媒體公司有自己的偏見)，還可以查看各種媒體在不同時間的偏見如何變化問題。 這樣做的目的不僅是要像Snopes.com一樣，成為可靠的媒體偏差量化來源，而且還要使人們和社區團結起來，形成一個兩極分化，無氣泡的世界。

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.

媒體偏見被認為是通過兩種機制來運作的：對問題的選擇性覆蓋(稱為問題過濾 )和如何呈現問題(稱為問題框架) 。 同樣，也沒有商定的方法來量化媒體的偏見。 MVP將基于研究論文“ 公平與平衡？ ”結合了機器學習和眾包技術來有效解決此問題。

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.

為了解決量化問題框架的問題，將構建一個基于NLP的分類器，該分類器將文章分類為“政治”和“非政治”類別，并進一步分類為新聞，觀點以及醫療保健等問題的子集，經濟等。此分類器將用于從我們的網站/應用中過濾掉非政治性內容，并標記有問題的文章或將其標記為新聞或觀點。 對于問題框架，基于內容的量化方法通常比基于受眾的方法更為可取。 該網站/應用將通過眾包內容分析每天跟蹤媒體的偏見。

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.

進入PopTheBubble的用戶將看到匿名的政治文章。 閱讀每篇匿名文章后，將要求用戶按從左傾斜到右傾斜的等級對其進行評分。 所有這些結果將反過來用于量化問題框架中的偏差。 通過這種方式，不僅可以在出口級捕獲媒體館的傾斜，而且可以在發行級捕獲。

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.

人們想要公正的新聞，并愿意探索提供新聞的新平臺。 許多人也更喜歡匯總各種基于問題的政治新聞，但如今新聞渠道，甚至社交媒體(通過其“個性化”新聞源)很少適度。 他們撰寫或宣傳的文章可能偏右或偏左，這可能會提供歪曲的現實觀。 因此，人們將使用PopTheBubble來檢查媒體渠道的極性，并獲得意識形態上不同的新聞和觀點。

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.

以上討論的方法將允許創建一個實時跟蹤偏見/偏見的網站/應用程序，而其他任何網站或應用程序現在都沒有。 將來，PopTheBubble可以擴展為多種用途，包括為較小的媒體公司提供發布文章的平臺，成為希望實時獲得結果的大型媒體公司的預印偏差檢查工具，并允許人們表達自己的意見。在文章上。 該初創公司將是面向媒體公司的軟件即服務(SaaS)和面向消費者的產品。

競爭對手： (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夠檢查和跟蹤大型媒體公司的偏見。 即使進行徹底的搜索，市場上也沒有大公司。 功能相似的兩個站點是allsides.com和mediabiasfactcheck.com 。 盡管allsides.com從不同角度對事實進行分類和呈現，但它們只允許在媒體級別進行投票/評分(這是不公平的)，并且無法對這些文章進行互動或投票。 此外，他們仍然顯示撰寫文章的新聞媒體。 Mediabiasfactcheck.com幾乎是靜態的，不會讓用戶發表意見。 盡管類似，但這些網站與我們的目標相去甚遠。 目前，許多新聞公司都有一個內部系統來檢查其內容的偏差，但沒有第三方眾包提供者。 從長遠來看，PopTheBubble可能就是這樣。

用戶獲取： (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名用戶將通過我們的專業網絡和社交網絡(LinkedIn，Facebook，Instagram，Twitter)以及在LinkedIn和Medium上撰寫文章來吸引人們，使他們意識到我們的平臺。 一旦所有社交媒體資源用完，將探索在線廣告的選項。

前兩周的里程碑： (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.

一個簡單的網站，可匯總所有新聞，并允許用戶以從左傾到右傾的比例對其進行評分。 涉及的步驟將是通過API收集數據并將其聚合，然后使用AWS lambda將其自動化。 同時，我們的兩名開發人員將開始為網站構建React后端，其中一名開發人員將為該應用程序學習react-native。 另外，將花費一些時間來創建模型和設計系統。 我們的NLP分類器應具有標記的數據和最終模型，以便在兩周后進行訓練。

技術細節： (Technical Details:)

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

正如產品構想中所述，我們將通過基于內容的眾包解決量化和跟蹤媒體偏見的問題：

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.
   美國流行的媒體網站(約20個)將每天或每小時被刪除。 可以使用通用新聞API(例如Google新聞API，新聞API，Bing搜索API等)來完成此操作，也可以使用特定的媒體機構(例如新聞紐約時報API，BBC新聞API等)來完成。

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.

   我們通過分類器將其分類為政治或非政治，新聞或觀點，并在政治領域內對問題進行標記等。要構建分類器，我們將需要在標記數據上訓練基于NLP的模型。 我們打算使用已經可用的數據集(例如Kaggle上的“ 新聞類別數據集 ”)并使用從步驟1中從API獲取的數據來創建我們自己的數據集。 如果創建自己的標記數據集，則標記任務將在Amazon MTurk上眾包。

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.
   對文章進行匿名處理，然后將其呈現給在我們的網站上閱讀并評價為左傾，右傾等(在一定范圍內)的用戶。 我們NewsFeed的顯示策略可以是基于流行度，基于問題或僅基于時間戳。
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.
   使用步驟2的分類結果和步驟3的眾包結果來量化，顯示和跟蹤媒體偏見。 這樣，我們可以精確地量化并顯示問題篩選和問題框架中的偏見如何隨新聞/意見級別和問題級別上的每個媒體整體隨時間變化。

用這個想法可以預期的挑戰： (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.

該產品將來可能面臨的最大問題之一是新聞媒體和媒體公司的潛在隱私/法律問題。 我們正在匿名顯示來自許多大型新聞公司的文章，這些文章需要產品中的某種認證或參考。 為了解決這個問題，我們計劃包括一個“ View Publisher”切換按鈕，該按鈕允許用戶查看對生成該用戶正在查看的文章的特定新聞媒體的引用。 由于查看文章的參考文獻會導致偏見，因此，一旦按下此切換按鈕，用戶就無法查看文章。

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.

我們的第二個挑戰將是貨幣化。 貨幣化計劃將極大地吸引早期投資者并引導我們的財務狀況。 通過開發和部署我們的想法，我們正在收集的信息和見解對于媒體而言可能是無價的。 通過對消費者輸入產品的分析和見解，我們可以利用特定文章上的信息，并與媒體聯系以潛在地出售此信息或為他們充當MTurk！ 另一個方便的資金來源是通過廣告(通過Google Ads或其他供應商)。 隨著客戶群的增長(1000多個用戶)，我們可以通過在產品中投放廣告來利用我們的可銷售性和拓展性。 我們產品的增長將與產品帶來的收入相關。

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!

我很想聽聽您對產品創意的想法。 我是產品管理界的新手，非常感謝您的反饋！ 您可以在LinkedIn上找到我或在下面發表評論。 感謝您的閱讀！