Group_5 Section C.docx

MANAGEMENT INFORMATION SYSTEMS

ASSIGNMENT Twitter as a Structured Information System

Submitted to: Prof H.K Misra

Submitted by: Group 5 Manoj (P39142) Suraj (P39171) Yogendra (P39176)

Introduction Twitter is an American online news and social networking service on which users post and interact with microblogs known as Tweets, Tweets are restricted to 140 characters. It was launched in July 2006 by Jack Dorsey, Noah Glass, and Evan Williams. Twitter Inc. is based

in San Francisco, California, has more than 25 offices and 3372 employees around the world. In the past few years, Twitter has achieved tremendous success. As one of the three most popular social networking sites (Facebook, Twitter, LinkedIn), it has evidently found the path to its user, more than 200 million users and 155 million posted messages a day, Twitter has become a vast storehouse of content, i.e. information and knowledge. Twitter is also being used for marketing and customer service purposes as twitter gives you access to a real-time update of what's going on in your industry and around the world. Twitter is really one of the lowest barriers of entry to connect with potential customers, brand advocates and influencers. Twitter is now stepping up to global television. Microblogging is the posting of very short entries or updates on a blog or social networking site, typically via a cellular phone. Twitter allows its users to keep their friends and family informed of their current status.

Mission Statement Reach the largest daily audience in the world by connecting everyone to their world via our information sharing and distribution platform products and be one of the top revenue generating Internet companies in the world. Vision Statement We believe in free expression and think every voice has the power to impact the world. Values Grow our business in a way that makes us proud. Recognize that passion and personality matter. Communicate fearlessly to build trust. Defend and respect the user's voice. Reach every person on the planet. Innovate through experimentation. Seek diverse perspectives. Be rigorous. Get it right. https://about.twitter.com/en_us/values.html

Organization Structure & People In Twitter, top management is divided their roles department wise. Top management mostly have Directors, CEO, COO, CFO, VP of Marketing, Managing director, chairman of board.

Twitter Focusing on growth while maintaining an open platform demands a skilled executive team that embraces a challenge. It also requires a board of directors that promotes our mission as well as protects our financial goals. At Twitter, we are fortunate to have both. Twitter has many departments and each department working independent. Every department is assigned to one person who is responsible for taking care of department.

Case Background In the past few years, Twitter has achieved tremendous success. Twitter is a free online microblogging service having more than 200 million users and 155 million posted messages a day, Twitter has become a vast storehouse of content, i.e. information and knowledge. Such a large amount of data that can be accessed on Twitter requires a structure in order to help users find the information they need. Twitter is a very simple tool which does not offer the possibility of structuralizing this vast amount of data, but it allows users and third parties to create different applications that can help structuralize the content. The most popular ways of structuralizing the content on Twitter are the community conventions: the hashtags (#) and the @ symbol, which pull together all the tweets with these symbols. The hashtags mark specific topics and the @ symbol helps to gather all the tweets a person was mentioned in. Twitter has a very simple and straight-forward nature; it does not offer many different ways of structuralizing information but it allows others to create interesting applications. Twitter is also being used for marketing and customer service purposes as twitter gives you access to a real-time update of what's going on in your industry and around the world. Twitter is really one of the lowest barriers of entry to connect with potential customers, brand advocates and influencers. It highlights the "instant" information delivery, the feature includes promoted tweets for brands, rewards quantity instead of the quality of the information. Which makes difficult to structure the data in order to provide relevant information to a consumer.

ORGANIZATION LIFE CYCLE There are a lot of exciting times to be a marketer nowadays. We are living in an era where the amount of mediums available to us has never been larger, whereas metrics and analytics tell us what works and what doesn’t, typically in real time. Twitter is a marketer’s dream tool. Real-time, real individuals, spewing forth details regarding what they like, do not like, and what matters to them. It’s seen large uptake since its origin in 2006 with some help from Barry, Ashton, and CNN. With over 44 million new guests per month and counting, the audience is growing, and corporations and organizations recognize that they need to have interaction or be left behind. Growth stage is simple to predict. Popular things become a lot of popular and other People adept at an increasing rate. All the hype, a glamour that is piled on Twitter have enabled it to remain within the news and grow exponentially, however, it'll eventually taper off the subsequent few years. It’s what happens next, the Maturity part that's a lot of fascinating. As the market matures and a lot of significant information is collected, value creation and measurement will become the key social media trends, not simply what tools your company is utilizing.

Twitter’s

Quarterly

Net

Income

It took Twitter quite a decade to become profitable, however currently it looks like those profits are here to remain. Twitter reported its fourth straight profitable quarter in 2018. In the past four quarters, Twitter’s net income is simply over $1 billion. Thus, the company is in growth phase.

Porter’s Five Forces Analysis of Twitter

Porter’s five forces model delves into the strategic assessment capabilities of the organizations to ensure better life cycles in the environment. Twitter is one of the top 10 social media websites. These are the detailed Porter Five Forces Analysis: Competitive Rivalry – High Social media industry now a days has been highly competitive with the rise of many new players such as Twitter, Facebook, and Myspace etc. However, most of these industry fail to reach the fan base of Twitter due to high entry barriers of the industry. The Social media industry is continuously going under technical advancements and addition of new features to attract users. The player who fails to innovate or add new features gets kicked out of the industry. Small players that are able to make an entry are generally acquired by the giants. Twitter is facing a strong competitive rivalry.

Threats of New Entrants – Low Social media companies relies on its level of innovation, a number of users, and its age. Twitter and existing players have attained extensive technology, made their websites and applications highly innovative attracting the masses towards them. Twitter has developed a brand image and loyalty. The success of new entrants is very difficult and making this threat very low for Twitter. Bargaining Power of Buyers – High In Twitter case, the bargaining power of buyers in this industry consider high This is because others social networking site such as Facebook, WhatsApp, WeChat, Instagram, and Google are also provided similar features such as sharing video, and photos, view the latest news and information with Twitter to their customers. Therefore, the switching cost for users to use other social networking site is relatively low to Twitter, the possibility of switching to other social networking site is high because all these application can be downloaded for free, it also consider easy to use and understand. The buyers of Twitter are different business and marketers who run advertisements on Twitter by paying Twitter. Since there are other social media players in the market that are also

running through funds from such advertisements, Twitter is in no position to attempt to raise advertisement prices in an attempt to gain greater revenue. Also buyers have low switching cost thus, buyers hold a strong buying power over it. Bargaining Power of Suppliers – Low The bargaining power of suppliers in this industry consider low. This is because Twitters’ investors injected a total of $155 million to Twitter in 2009. Some of the investors included Institutional Venture Partners, Benchmarks Capital, Union Square Ventures, Spark Capital, Digital Garage, Bezos Expeditions and Insight Venture Partners, and others. Twitter has more power to reject and able to continuing improve their products and services provided to their customers without any restriction from the suppliers.

Threats of Substitutes – High There are larger number of substitutes in the social media industry. There include Facebook, Instagram, Google etc. All are competing to increase the number of users on their platform and increase in their market share. Twitter offers a limit of 140 characters per Tweet, its substitutes are at an advantage as none of them hold this limit. This makes the threat of Substitutes for Twitter very high. Chaos Theory Chaos Theory studies the effect of complex systems, those that are nonlinear and subject to many variables. Chaos is a state of primal emptiness which is governed by simple and precise laws, but where the outcome is unpredictable and may change greatly with slight variations in starting conditions. Social networking sties like Twitter, FB, are variable in nature. Because of that it is termed as the ‘butterfly effect’ as one small action can create a dynamic and increasing change in any system. For instance you create a post on any subject Twitter, which comment could grow so many new added comments it is featured and becomes a trending topic. People take advantage of Chaos Theory and become the social media butterfly by using twitter as Search out influential people in your niche by using Twitter Search, Re-post good posts, especially those by influential people and become a trusted source of great information.

MACRO ECONOMICS THEORY This segment refers to the nature and direction of the economy in which a firm competes or may compete. Generally, a firm will choose to compete in a relatively stable economies with strong growth potential. There is a strong intensity of rivalry among Twitter and Google or

Facebook, all of these social networking site are competing in America, which is a country with a relatively stable economies and have a strong growth potential. According to this theory, contemporary organization consider IT as a factor of production. Twitter uses internet and technical equipment in their plant. Twitter ensures better monitoring and controlling in their application to ensure that consumer will not get any issues later. RESOURCE BASED VIEW The field is related to strategic management. In RBV it is argued that organisations garner the competitive advantage through strategic use of their internal resources and by understanding changes in the external environment. The competitive advantage is related to “cost leadership” and differentiation through setting new standards for its products and services, to gain access to critical law material, locations production capacity and customers. Twitter differentiate their product by regular innovation, Twitter is fast-paced, concise, and easy way to get connected to the audience. Companies with larger market capital are more likely to adopt Twitter for static advertising, a one-way communication approach, and direct interaction with consumers, a two-way communication approach. Companies with smaller market capital that adopt Twitter for marketing communication appear to disseminate more corporate promotion information, a static advertising approach. Overall, our results indicate that the level of a company's access to resources can influence their adoption of a new technology and the manner in which it is used.

PROCEDURE IN TALLY The following process is followed to generate balance sheet in Tally (ERP 9): 

Created a new company with name TWITTER



We have entered values in rupees, while preparing the balance sheet and the



In the first step, we have created all the ledgers related to balance sheet and also put the respective opening values of the balance sheet. As we have taken the financial year 2017-18, we have inserted the closing balance of 2017-18 in the ledgers.



After creation of opening balance sheet, we have created ledgers pertaining to the profit and loss account. We need to create these ledgers with no values inserted (i.e. no initial values).



We have generated payment and receipt vouchers related to the profit and loss account as on 31st March 2018.



Then ‘period’ was adjusted to produce final balance sheet. Both final balance sheet for 2017-2018 and profit and loss account was made into Excel sheet using ‘Export’ option in Tally.



Other assets were considered as written under the head current assets

Twitter Profit & Loss A/c For 1-Apr-2018 Particulars

Twitter For 1-Apr-2018

Purchase Accounts Cost of Goods Sold Gross Profit c/o

964997.0 0

2077362. 00 3042359. 00

Indirect Expenses Income Tax Interest Expenses Research and Development Sales and

Particul ars 964997.0 Sales 0 Accounts Sales

782052.0 0 132606.0 0 553858.0 0 1070179.

Gross Profit b/f 974591.0 Indirect 0 Incomes Additional Income

Twitter For 1-Apr-2018 3042359. 00 3042359. 00 3042359. 00 2077362. 00 102825.0 0 102825.0 0

Distribution Nett Profit

00 1205596. 00 2180187. 00

Total

Total

2180187. 00

Twitter Balance Sheet For 31-Dec-2017 Liabilities Capital Account Reserves & Surplus Retained Earnings Loans (Liability) Common Stock Long Term Debts Other Equity Current Liabilities Accounts Payable Laibility Charges Other Debs Short Term Debts Profit & Loss A/c Opening Balance Current Period Total

Twitter as at 31-Dec-2017 Assets 6870901.0 Fixed Assets 0 8324974. Asset Charges 00 Fixed Assets 1454073. 00 1690009.0 Good Will 0 4.00 Intangible Assets 1755316. 00 65311.00

1227269. 00 45025.00

Current Assets

7196741.0 0

Closing Stock 1601662.0 0

550937.0 0 17849.00 67502.00 965374.0 0

Twitter as at 31-Dec-2017 2965831.0 0 808459.0 0 885078.0 0

Cash-in-Hand Debtors Investment

Other Assets Other Current Assets 1205596.0 Difference in 0 opening balances

1894444. 00 788700.0 0 4314957. 00 85705.00 112935.0 0 1205596.0 0

1205596. 00 11368168. 00

Total

11368168. 00

Cybernetics more broadly encompasses the study of how systems regulate themselves and take action toward goals based on feedback from the environment. These systems are not just computational; they include biological (maintaining body temperature), mechanical (governing the speed of an engine), social (managing a large workforce), and economic (regulating a national economy) systems [1]. In addition to reaching goals, AI and cybernetics both consider how systems can learn; however, while AI considers using stored representations as a means of acting intelligently, cybernetics focuses on grounded and situated behaviors that express intelligence and learning based on feedback and interaction [2]. Social content recommendation has risen to a new dimension with the advent of microblogging platforms like Twitter, FriendFeed, Dailybooth, and Tumblr. As the number of people using such platforms are increasing on a daily basis, there is a rapid growth in the amount of data and information gathered using such microblogs. Although, this uproar of data provides us with a “gold-mine” of real-world information, it is not without it’s side effects; it has lead to a major problem called the information overload (Borgs et al. 2010). The most critical problem that branches out from the information overload is the difficulty in organizing the timeline of users. For example, an active twitterer follows 80 users on an average, and receives over 1000 tweets (Qu and Liu 2011); Copyright c  2014, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. due to such an incessant flooding of user-timeline, many important and interesting tweets remain unnoticed by the users. Furthermore, this results in an increase of irrelevant and personal tweets that are not worth reading. Researchers have tackled the problem of information overload from various different perspectives such as organizing trending topics in user’s timeline, URL recommendations for twitterers, recommending followers and tweets (Bernstein et al. 2010; Abel et al. 2011; Armentano, Godoy, and Amandi 2012; Hannon, Bennett, and Smyth 2010). A new direction of research that is proposed in this paper is the development of personalized recommendation based on social lists. Lists serve a dual purpose in various social networks. First, they serve as a newsletter or a daily-digest for users who seek unified source of information. Second, they act as topicalhubs that unite users who share similar interests. Originally lists were introduced by Twitter in 2009; however, they have

been adopted by various social networking websites in different forms under different names. For instance, Google+ terms lists as social circles and Facebook provides a feature called community pages. In general, every list has a curator who creates the list and makes it as private or public. Other users can freely subscribe to such public lists, while private lists are restricted to the owner’s approval. Lists are one of the strongest indicators of topical homophily (Kang and Lerman 2012). Consequently, they can be an excellent tool to smoothen the problem of information overload. Recommending lists is a challenging task because most users create them for grouping friends or other users whom they find interesting. Such lists that are created for personal convenience do not gain the attention of people. This implies that most of them do not have any subscribers. Furthermore, list names are not unique; there can be thousands of lists with similar (or even same) names (Kim, Jo, and Moon 2010). This further exacerbates the problem of finding genuine, authoritative and topically relevant set of lists. In this paper, we propose two recommendation models that recommend lists for Twitter users based on their personalized interest. Our first model, called the ListRec, captures and models the users’ interest based on a combination of content, network and trendiness based measures. For users with rich tweet history, we measure their interests using the topics derived from their tweets. Unlike the existing studies, we view the twitterer’s interest as a temporally varying feature and exploit this variation using an exhaustive set of streaming tweets to dynamically model the users’ interest. For users with sparse tweet history, we project the user space into a followee space and utilize the followee’s list subscriptions to indirectly measure the interest of the users. We also add a new trend based score that measures the popularity of lists in the Twitter domain. The final score is then modeled as a linear combination of these three individual scores (based on content, network, and popularity) to effectively measure the interests of the users and personalize list recommendation. The coefficients in this linear combination are estimated using a cyclic ridge regression estimation approach. Our experimental results show that the ListRec outperforms other competing state of the art methods. Our second model is the LIST-PAGERANK which will recommend lists that are popular and are more (topically) authoritative than the lists that are currently subscribed by the users. To the best of our knowledge, there are no studies that use Twitter lists for personalized recommendation. We summarize the major contributions of this paper as follows: a. We propose a recommendation framework called ListRec that recommends Twitter lists based on the

personalized interest of twitterers. Unlike the existing studies that recommend external information like news articles and blogs, our work is purely domain-specific. b. The interests of users are modeled using a combination of weighting schemes: (a) a content based scheme that models the users’ interest based on temporally varying topics; (b) a network based scheme that uses the followeenetwork of the users to overcome the tweet sparsity; and (c) a trendiness based scheme that is based on the popularity of the lists. c. We propose a LIST-PAGERANK based algorithm that leverages the network structure of Twitter lists to recommend authoritative lists that match the topical interest of the users. The rest of this paper is organized as follows. We begin by describing the modeling of ListRec in Section 2. Section 3 describes the creation of the list network and formulation of the LIST-PAGERANK. Section 4 will show the results of our experiments and explain the data collection methodology. Section 5 discusses the related work on this topic. Finally, the conclusions obtained through this study are presented in Section 6 Social content recommendation has risen to a new dimen-sion with the advent of microblogging platforms like Twit-ter, FriendFeed, Dailybooth, and Tumblr. As the number ofpeople using such platforms are increasing on a daily basis,there is a rapid growth in the amount of data and informa-tion gathered using such microblogs. Although, this uproarof data provides us with a “gold-mine” of real-world infor-mation, it is not without it’s side effects; it has lead to a majorproblem called the information overload (Borgs et al. 2010).The most critical problem that branches out from the infor-mation overload is the difficulty in organizing the timeline ofusers. For example, an active twitterer follows 80 users on anaverage, and receives over 1000 tweets (Qu and Liu 2011);Copyright c2014, Association for the Advancement of ArtificialIntelligence (www.aaai.org). All rights reserved.due to such an incessant flooding of user-timeline, manyimportant and interesting tweets remain unnoticed by theusers. Furthermore, this results in an increase of irrelevantand personal tweets that are not worth reading. Researchershave tackled the problem of information overload from var-ious different perspectives such as organizing trending top-ics in user’s timeline, URL recommendations for twitterers,recommending followers and tweets (Bernstein et al. 2010;Abel et al. 2011; Armentano, Godoy, and Amandi 2012;Hannon, Bennett, and Smyth 2010). A new direction of re-search that is proposed in this paper is the development ofpersonalized recommendation based on social lists. Listsserve a dual purpose in various social networks. First, theyserve as a newsletter or a daily-digest for users who seekunified source of information. Second, they act as topical-hubs that unite users who share similar interests. Originallylists were introduced by Twitter in 2009; however, they havebeen adopted by various social networking websites in dif-ferent forms under different names. For instance, Google+terms lists as social circles and Facebook provides a featurecalled

community pages. In general, every list has a cura-tor who creates the list and makes it as private or public.Other users can freely subscribe to such public lists, whileprivate lists are restricted to the owner’s approval. Lists areone of the strongest indicators of topical homophily (Kangand Lerman 2012). Consequently, they can be an excellenttool to smoothen the problem of information overload.Recommending lists is a challenging task because mostusers create them for grouping friends or other users whomthey find interesting. Such lists that are created for personalconvenience do not gain the attention of people. This implies that most of them do not have any subscribers. Further-more, list names are not unique; there can be thousands oflists with similar (or even same) names (Kim, Jo, and Moon2010). This further exacerbates the problem of finding gen-uine, authoritative and topically relevant set of lists.In this paper, we propose two recommendation modelsthat recommend lists for Twitter users based on their per-sonalized interest. Our first model, called the ListRec,captures and models the users’ interest based on a combina-tion of content, network and trendiness based measures. Forusers with rich tweet history, we measure their interests us-ing the topics derived from their tweets. Unlike the existingstudies, we view the twitterer’s interest as a temporally varying feature and exploit this variation using an exhaustive setof streaming tweets to dynamically model the users’ inter-est. For users with sparse tweet history, we project the userspace into a followee space and utilize the followee’s listsubscriptions to indirectly measure the interest of the users.We also add a new trend based score that measures the popularity of lists in the Twitter domain. The final score is thenmodeled as a linear combination of these three individualscores (based on content, network, and popularity) to effec-tively measure the interests of the users and personalize listrecommendation. The coefficients in this linear combinationare estimated using a cyclic ridge regression estimation ap-proach. Our experimental results show that the ListRecoutperforms other competing state of the art methods. Oursecond model is the LIST-PAGERANK which will recom-mend lists that are popular and are more (topically) authori-tative than the lists that are currently subscribed by the users.To the best of our knowledge, there are no studies that useTwitter lists for personalized recommendation. We summa-rize the major contributions of this paper as follows:a. We propose a recommendation framework calledListRec that recommends Twitter lists based on thepersonalized interest of twitterers. Unlike the existingstudies that recommend external information like newsarticles and blogs, our work is purely domainspecific.b. The interests of users are modeled using a combination ofweighting schemes: (a) a content based scheme that mod-els the users’ interest based on temporally varying top-ics; (b) a network based scheme that uses the followee-network of the users to overcome the tweet sparsity; and(c) a trendiness based scheme that is based on the popu-larity of the lists.c. We propose a LIST-PAGERANK based algorithm thatleverages the network structure of Twitter lists to recom-mend authoritative lists that match the topical interest ofthe users.The rest of this paper is organized as follows. We begin bydescribing the modeling of ListRec in Section 2. Section 3describes the creation of the list network and formulation ofthe LISTPAGERANK. Section 4 will show the results of ourexperiments and explain the data

collection methodology.Section 5 discusses the related work on this topic. Finally,the conclusions obtained through this study are presented inSection 6

The Strengths and Weaknesses of Twitter Strengths: Following Twitter is just ideal for following interesting people or topics. Users enjoy following people they don’t know personally, and they often look at hashtags to keep up with issues that interest them. Paying some attention to the type of content that you post can even make your tweets go viral. Hashtags on Twitter are also great for creating temporary communities around events. Brevity With only 140 characters, Twitter is ideal for those with short attention spans! Followers are able to quickly scan and decide what they want to click on to learn more about. Likability If you are all about pleasing others and building their trust, then Twitter is just right for you. Twitter allows you to show your true personality and to make your audience happy. You are able to reveal, not just your area of expertise but also how you interact with others. Try offering free samples, but then inviting your followers to your website to make purchases or to find out more. Remember that relationships are important, so be careful how you conduct yourself. Using any social media after drinking excessively is always a bad idea. Relationships One of the greatest things about Twitter is that you can easily initiate contact with anyone. It makes it very easy to quickly build relationships that may blossom into real-life business connections. This is much harder with LinkedIn, which requires you either to already know the person, be in the same group, or get someone to introduce you. But on Twitter, you can follow anyone, although, Twitter lacks the handy contact database that you can access via LinkedIn.

Both sites have their advantages, but you will have to be proactive about building relationships and initiating contact with those whom you want to connect. Weaknesses: Too much Information Due to the immediacy of Twitter, it’s easy to miss out, especially if you follow a lot of people. It is simply impossible to catch all the content which appears in your Twitter feed. Conversely, it’s likely that only a small percentage of your followers will see your tweets. Because of the highly temporary nature of tweets, you can only see them for a brief moment, although, they are permanently searchable. You may need to repeat your Tweets to make them last longer, whereas the functions available in LinkedIn (status updates, group discussion, and internal messaging), makes it easy to refer back to posts. If you are just looking for immediate feedback, Twitter is great, but for longevity, go with LinkedIn. At the same time, remember that you can make your tweets funny, punchy, and memorable. Brevity The 140-character limit is both a blessing and a curse! Some may love it, but there are times when it’s very difficult to condense all your important information in a tweet. Platform Twitter has come a long way in upgrading its infrastructure to handle traffic loads and to reduce appearances of the “fail whale.” But most of the innovations that make Twitter more user-friendly seem to come from users that want to build a client base rather than within Twitter itself. Some make use of a third party service but this can be risky. When Twitter purchased Backtweet, for example, they shuttered its API.

Statistics and Analytics If analyzing the effect of social media on your business is important, Twitter may not lend itself well to that. While you can certainly measure the effect of your own tweets, measuring how Twitter influences your business may be much more difficult. Much of Twitter’s traffic comes from desktop and mobile clients, so tracking it may be very hard. Twitter’s systemic issues with being entirely unable to deal with any sort of abuse whatsoever are well-documented (I’d argue that, with the executives being Freezepeach Libertarianbros

Disadvantages On the other hand, many Twitters’ users face the privacy and confidential issues, this is because Twitter allow its users to freely sign up then log into Twitter and view all public tweet or information they want. This make its users feel unsecure while using Twitter, because they will feel like sharing their own personal information with third parties. Besides, this will cause the online fraud cases to increase because of lack of personal privacy that offered by Twitter. Furthermore, another weaknesses of Twitter is that Twitter still remain essentially text-based and allow its subscribers to send “tweets” of 140 characters or less to their “followers”. This is because some of the users may not be able to express their feeling or view point within 140 characters. As a result, Twitter had a lot of dead or not active subscribers, this is because they are not able to connect and adapt to the philosophy of express their feeling within 140 characters. Fake news promotions because of algorithms Fake news and misinformation should not violate the democracy of country