Social Is the New Financial: How Startup Social Media
Activity Influences Funding Outcomes
February 7th, 2017
Fujie Jin
Kelley School of Business,
Indiana University
jinf@indiana.edu

Andy Wu
Strategy Unit
Harvard Business School
awu@hbs.edu

Lorin Hitt
The Wharton School,
University of Pennsylvania
lhitt@wharton.upenn.edu

Abstract
Early stage firms increasingly use social media to communicate with their target stakeholders, such as
customers and investors. In this study, we investigate whether the use of social media is associated withincreased success in raising venture capital financing. We argue that social media can improve startup
funding success through two channels: 1) enabling investor discovery of potential investment opportunities
through reduction of search costs and 2) providing additional information to investors for a better evaluationof the quality of the ventures. Using social media activities on Twitter and venture financing data from
CrunchBase, we find that an active social media presence and strong Twitter influence (followers, mentions,impressions, and sentiment) increase the likelihood a startup will close the round, the amount raised, andthe breadth of the investor pool. In addition, we find that startup social media activity is associated withmore investment from investors with less information channels (e.g., angels) and making less industry
specialized investments in particular, consistent with the hypothesis that social media improves aninvestor’s ability to discover potential investments. Also, the effect size of social media is stronger for
startups where quality information is less available, such as firms outside geographic venture capital clusters
or where later investors do not have network relationships with early investors, consistent with social media
acting as an additional information channel to inform startup quality evaluation.
Keywords: Social Media, Startups, Venture Capital, Search Cost1

Introduction
With 72% of U.S. Internet users on Facebook and 23% on Twitter, social media has become an
important conduit of information for individuals, firms, and markets. Social media provides an alternative
channel for marketing communication, enabling firms to build their brands and interact with customers.
The effectiveness of social media for marketing goods and services has been particularly well-studied in
the context of established firms and product markets (Aral, Dellarocas, & Godes, 2013; Bharadwaj, El
Sawy, Pavlou, & Venkatraman, 2013). However, few studies have looked at the use of social media by
emerging firms or the role of social media in the capital markets. In this project, we explore the intersectionof these two areas to examine whether social media activities improve a startup’s ability to raise capitalfrom venture capitalists and angel investors, arguably one of the most important factors in the success ofearly stage firms.
The market for early stage private financing faces two distinct information challenges, both ofwhich social media can address. First, startup firms seeking private equity or debt financing are not listed
in centralized exchanges as are publicly traded firms, and investors need to engage in costly search toidentify potential startups to finance. Startups that are “off the radar” due to their location or lack of existing
relationships between the management team and potential investors are less likely to receive funding
without alternative means of communication. Second, startups cannot provide much of the traditional
information that investors use to evaluate firm quality, such as assets and cash flows histories, making it
difficult for investors to evaluate their quality (Aldrich & Fiol, 1994). This problem is exacerbated by aprincipal-agent conflict where entrepreneurs have an incentive to exaggerate growth and earnings to attract
investors and increase their equity valuation (Dessein, 2005). These information challenges can partiallybe attenuated by geographic agglomeration of investors and startups (Saxenian, 1991) that allows for morecontact between investors and startups, and information networks among investors themselves (Hochberg,
Ljungqvist, & Lu, 2007). Here social media provides an additional source of information that does not relyon geography or existing social networks among investors.

2Social media acts as a medium for information exchange and offers solutions to both the costly
search and information scarcity problems that market participants face. Startup firms can broadcast
information about themselves over social media and thus raise awareness of their existence among potential
investors, helping investors discover more early stage ventures and expand their consideration set of
potential investment opportunities. In addition, startups’ social media activities provide an additional
channel of information for investors to use when evaluating investment opportunities. For example,
popularity on social media could demonstrate a startup’s ability to attract specific customer groups, build
its brand name, and integrate feedback from consumers. Such positive social media information shows firm
quality to investors and raises their expected return on the investment, and thus increases the startup’s
chances of getting funding successfully. Anecdotally, venture capitalists are increasingly conducting “duediligence” on social media platforms and reacting favorably to startups with effective social media
performance. For example, Vandaele Capital LLC decided to fund Boxtera, a startup that delivers healthfood packages to subscribers, because of their effective use of Twitter to reach their target audience.1
Regulators are also taking note of social media’s growing role as a conduit for investment
information. Historically, startups were restricted in their ability to make public offers or solicitations tosell securities, including on social media platforms. However, the substitution of social media for traditional
information sources, such as press releases, has introduced ambiguity into the definition of appropriate
communications to potential investors. Along with the implementation of various other provisions of the
J.O.B.S. Act,2 the U.S. Securities and Exchange Commission (SEC) issued a new policy in June 2015
allowing startups to tweet to potential investors about the opportunity to invest in them, something they
were previously prohibited from doing on most public platforms. As this communications channel gains

1 Wall Street Journal. “If You Look Good on Twitter, VCs May Take Notice”. September 30, 2013.

http://www.wsj.com/articles/SB10001424127887324659404578499702279196058
2 Another provision of the Jumpstart Our Business Startups (JOBS) Act is the legalization of equity crowdfunding, the online
offering of private equity securities to investors. While various forms of crowdfunding are likely to occupy a growing persistentcomponent of the market for early stage equity financing, traditional venture capital and angel financing are expected to continue
to dominate early stage private equity financing market for the foreseeable future. Nevertheless, our research on the implication ofsocial media for venture capital and angel financing should also have implications for the future of social media in equity
crowdfunding as well.

3

further legitimacy, it is becoming more important that we understand the policy implications for early stage
venture financing markets and entrepreneurial performance.
Few existing studies have looked at the use of social media by emerging firms or the role of social
media in the capital markets. Rather, existing studies on social media predominantly focus on marketingoutcomes and on established firms. Studies have shown that social media promotes word-of-mouth
information diffusion (Aral et al., 2013; Chevalier & Mayzlin, 2006; Dellarocas, Zhang, & Awad, 2007;
Forman, Ghose, & Wiesenfeld, 2008; Zhu & Zhang, 2010) and serves as a platform for greater consumer
engagement with a product or brand (Chen, De, & Hu, 2015; Ghose & Han, 2011; Goes, Lin, & Au Yeung,
2014; Li & Wu, 2014; Miller & Tucker, 2013). Recent studies further link social media activity and firm
performance through mechanisms of marketing effectiveness (Chung, Animesh, Han, & Pinsonneault,
2014; Goh, Heng, & Lin, 2013; Luo, Zhang, & Duan, 2013) and value extraction from social media
analytics (Hitt, Jin and Wu, 2015). However, there exist few studies directly examining the use of social
media by and its effect on early stage firms, with the notable exception of related work by Aggarwal et al.
(2012), who examine social media mentions of a firm (particularly on blogs) and venture financing; theyfind that negative electronic word-of-mouth has greater impact than positive word-of-mouth, and that theeffect on financing decreases as a firm progresses to later stages of financing. In this study, we examine thedirect use of social media by startup firms for corporate promotion on the likelihood of funding, focusing
specifically on the role of social media in changing the costs or benefits of physical proximity.
This study bridges the information systems literature and entrepreneurial finance literature,providing empirical evidence for the effect of startup firms’ social media activities on their funding
outcomes. We construct a unique data set that combines financing rounds data for high-technology startups,
as reported in CrunchBase, with historical data on Twitter activity by the same startups, from Topsy.com.
We empirically investigate distinct hypotheses from two mechanisms through which social media facilitates
entrepreneurial financing, i.e., how social media helps investors discover potential investment opportunities

4

though search cost reduction, and how social media activity provides an additional channel of information
for investors to assess startup quality.
We find that social media activity on Twitter improves a startup’s chance of closing a financing
round, raises the total amount of funding they receive in the round, and increases the number of investors
participating in the round, after controlling for various firm-level characteristics. In addition, we findevidence supporting both of the mechanisms through which social media can influence startup funding.
First, we find evidence that startup social media activity reduces search costs in the market forentrepreneurial finance. Startups active on social media are likely to attract a larger portion of angel
investors in early funding rounds; since angel investors are usually not full-time investors and have lessalternative channels for information about possible investment opportunities as compared to VCs, social
media plays a larger role in their discovery of potential startups to finance. Startups active on social media
attract a larger portion of investors with diverse investment portfolios rather than concentrated investmentsin specific industries. For such investors, social media provides a low-cost channel to improve theirawareness of startup activities across a range of industries. This is in contrast to investors who makerepeated investments in the same industries. They generally build up personal connections and other sourcesof information to learn about new investment opportunities, making social media’s role as an informationbroadcasting channel less significant.
In addition, we find that startups located outside the primary clusters of VC activity in the U.S.
(Boston, New York, San Francisco) engaged in effective social media activities experience a greater
increase in their financing round size. Therefore, increased funding from social media activity to startups
located in regions where it is harder for investors to inspect the startups in person, shows that positive
information on social media can reduce uncertainty in startup quality and improve investors’ valuation forthe startups.
Second, we find evidence that startups’ social media activities give investors information to better
evaluate potential investment opportunities. When information about the quality of the startup is low, such5

as when investors do not have a prior co-investor participating in the current round and serving as an
information source, social media plays a more important role in the evaluation of startup quality. We alsofind that startups active on social media are more likely to receive funding from experienced investors,
especially when they do not have trusted sources of quality information through investor syndicate
networks. Since experienced investors accumulated expertise analyzing startup quality from priorinvestments, they should be more effective in utilizing social media for assessment.
This study has several practitioner implications regarding social media’s role as an alternative
channel of information for startups and their investors. Specifically, entrepreneurs can take advantage ofthe new SEC regulations and leverage social media campaigns to seek investors. In addition to broadcasting
their presence to potential investors, entrepreneurs can focus their social media strategy on portraying a
positive brand image, demonstrating the ability to engage target customer segments, and sourcing
informative customer feedback. From the investors’ perspective, social media presents an alternative
channel for discovering potential investment opportunities, particularly if they do not have existing
connections or channels of information in certain industries. Investors can observe the social media
activities of startups they are considering for investment, and use this information to assist in theirinvestment decision.

Theoretical Background and Hypotheses
Private equity investments by venture capital firms and angel investors continue to be a dominantsource of financing for early stage, high-growth, high-risk, technology businesses. In 2014, annual venture
capital inflows topped $48 billion for these “startups”, representing the highest levels in over a decade.3
The two primary types of investors in this space are venture capital firms, which are professional asset
management firms that invest using funds put up by institutional or large private investors, and angel3

PricewaterhouseCoopers LLP and National Venture Capital Association. January 16, 2015.
http://nvca.org/pressreleases/annual-venture-capital-investment-tops-48-billion-2014-reaching-highest-level-decade-accordingmoneytree-report/

6

investors, who are high-net-worth individuals4 investing their own funds.5 These financial intermediaries
specialize in the evaluation, investment execution, and post-investment monitoring of startups.
Venture capital firms and angel investors face two unique information challenges in discoveringand evaluating investment opportunities. First, the lack of a centralized market for early stage private equity
means that entrepreneurs and investors lack information about the existence of parties on the other side of
the market, and thus they must undergo a costly search process (Inderst & Müller, 2004) in order to identify
a possible choice set before they can even begin the process of information collection and evaluation (duediligence). In other words, possible investors may not even be aware of a particular venture and ventures
may have limited knowledge of available funding opportunities. These search costs can be prohibitive,
preventing legitimate high-quality ventures from obtaining funding on acceptable terms.
Second, startups lack many of the traditional physical assets and steady cash flow histories used toevaluate more established businesses (Aldrich & Fiol, 1994), so investors have less information and facesubstantial uncertainty when evaluating a new venture (Kaplan & Strömberg, 2004; Shane & Cable, 2002).
The information problem facing investors is further exacerbated by an asymmetric information problem
between entrepreneurs and investors (Dessein, 2005), that entrepreneurs have an incentive to over-represent
the quality of their firm to investors in the hope of improving their chance at receiving an investment at ahigher valuation. Thus, hard information for the evaluation of new ventures is rare and the marginal valueof additional information is likely to be high (Amit, Glosten, & Muller, 1990; Gompers, 1995).
Social media can alleviate both of the above information problems by broadcasting information
about the existence startup seeking financing to potential investors and by offering another channel of
information for investors to evaluate startup quality through their social media activities.

Social Media and Organizations

4 In the United States, angel investors must be accredited by the SEC, meaning they must have a net worth of at least $1 million(not including the value of their primary residence) or have an income over $200k each year for the last two years.
5 New forms of entrepreneurial finance, such as peer-to-peer lending and crowdfunding, have developed in the last few years, butthey continue to be a niche segment of the capital market for early stage private equity in both scale and influence.

7

While the literature on social media use has focused on established firms, the same insights apply
to social media communications by early stage emerging firms.
Perhaps the most studied aspect of social media is its role as a new marketing channel to customers.
A substantial literature has linked feedback from consumers, such as product reviews, to product sales andchanges in marketing strategy (Dellarocas et al., 2007; Forman et al., 2008; Hong, Chen, & Hitt, 2014; Li& Hitt, 2008; Zhu & Zhang, 2010).

In addition, social media contributes to long-run marketing

performance, by providing an alternative channel for organizations to build brands and engage consumers(Ghose & Han, 2011; Goh et al., 2013; Lee, Hosanagar, & Nair, 2014; Rishika, Kumar, Janakiraman, &Bezawada, 2013; Shriver, Nair, & Hofstetter, 2013). Finally, social media serve as a platform for newmarketing strategies that encourage information diffusion and social influence through network ties (Angst,
Agarwal, Sambamurthy, & Kelley, 2010; Aral & Walker, 2011; Bapna & Umyarov, 2015). In addition to
documenting the direct performance of social media on marketing outcomes, a new stream of research links
these benefits translate to improvements in overall firm performance (Chung et al., 2014; Hitt, Jin, & Wu,
2015; Luo et al., 2013).
An emerging stream of work studies the impact of social media on entrepreneurs, with a focus on
online capital markets, such as crowdfunding or peer-to-peer lending platforms (Agrawal, Catalini, &Goldfarb, 2011; Lin, Prabhala, & Viswanathan, 2013). Eesley and Wu (2015) study the motivation forsocial network connections between entrepreneurs and their mentors and how these connections affect firm
performance. Greenwood and Gopal (2015) examine how media coverage of a technology segment
influences the number of new startups founded in that segment. Aggarwal, Gopal, Gupta, and Singh (2012)
identify a link between blog mentions and sentiment about startups and subsequent financing outcomes.
(Agrawal et al., 2011; Lin et al., 2013) study the peer-to-peer lending market and find that stronger social
network relationships are associated with a higher likelihood of a loan being funded, a lower risk of default,
and lower interest rates.

8

Taken broadly, existing studies suggest that the use of social media can influence overall firm
performance, including the success of early stage firms and their ability to obtain financing. A new
emerging trend of literature also finds evidence of firms’ active presence on social media leading toincreased market value, suggesting that firms’ activities on social media can also influence investors’
evaluation of firms’ equity value (Goh et al., 2013; Luo et al., 2013). While these studies mostly use datafrom established companies, we expect to see similar logic at work for startup firms. Based on the logic of
the aforementioned studies and the two mechanisms we will outline immediately after this, we hypothesizethat startups more active on social media are more likely to succeed in their funding process:Hypothesis 1: A startup active on social media is more likely to receive larger amounts offunding from investors.
Hypothesis 2: A startups active on social media is more likely to receive funding from a larger
number of investors.
The next two sections outline the two specific mechanisms, namely search cost reduction andstartup quality information channel, that together drive Hypothesis 1 and Hypothesis 2. We will also
describe distinct hypotheses from these two mechanisms that enable us to disentangle them.

Search Costs and the Discovery of Investment Opportunities
Unlike publicly traded companies, early stage startup firms do not have a centralized market where
investors have easy access to all potential investment opportunities. In fact, private equity investors andentrepreneurs engage in a costly search process to find one another (Inderst & Müller, 2004). Furthermore,
there exist few brokers connecting startups with investors, a role played by investment bankers in the caseof large mature firms. Search costs constitute a significant barrier limiting investor awareness of the full setof investable startups.
A long stream of information systems literature examines the role of IT in reducing search costs.
Digital communications technologies can substitute for geographic proximity, enabling firms to locate

9

“closer” to customers or their target markets without incurring significant costs of coordination or
uncertainty (Bakos & Brynjolfsson, 1993; Clemons & Row, 1992; Gurbaxani & Whang, 1991; Malone,
Yates, & Benjamin, 1987). Like prior advances in information technology, social media presents a similar
opportunity for lowering the cost of communication, which is particularly salient for early stage firms that
may not have developed more traditional marketing capabilities that require greater upfront capital
investment.
In this study, we focus on the social media site Twitter, whose open platform enables firms tobroadcast information to a targeted audience (Chen et al., 2015). Twitter’s platform design enables users tofollow anyone they wish, making it an effective channel to distribute information to a large group of alreadyinterested users (Fischer & Reuber, 2011). Just as established firms can use Twitter to reach to customers,
startup firms can also use it to broadcast about themselves and reach out to a larger pool of both customers
and investors. Since investors have easy access to information on Twitter, this low cost information channel
can broaden their pool of potential investment opportunities.
While social media information is equally available to all types of investors, it may be especiallyvaluable to investors lacking the formal information channels through which they source their set of
possible investment deals. Angel investors, who are not usually full-time investors, usually do not dedicate
a substantial amount of time to sourcing possible investments, and they do not have the support staff oraccess to institutional information available to venture capital firms (Lin, Sias, & Wei, 2015). However,angel investors should have comparable access to information arising from social media. Since angel
investors likely face higher search costs in the absence of social media, we expect to see social media
playing a larger role in their discovery of new investment opportunities.
Hypothesis 3: A startup firm active on social media is more likely to receive funding from moreangel investors.
Many investors concentrate their investments in a limited number of industries to better leverage
specialized expertise and business relationships that would help identify new opportunities (Sorenson &10

Stuart, 2001). Investors making investments in a few specific industries can rely less on social media todiscover new investment opportunities. On the other hand, for investors interested in making diverse
investments across different business categories, it would be costly to sustain a significant base of contacts
in each line of business to stay informed about potential startups to finance. Therefore, we expect social
media to play a larger role in the discovery of startups to invest in for investors with diverse portfolios.
Hypothesis 4: A startup firm’s social media activities have less effect on investors making
concentrated investments in certain industries.
Hypothesis 5: A startup firms active on social media is more likely to receive funding from moreinvestors with diverse investment interests.
Social media will player a greater role in reducing search cost when other information channels,
such as in-person interaction, are limited. Geographic distance makes it more difficult for investors to obtain
quality information on startups, since information circulates more freely between geographically proximate
people and firms (Rosenthal & Strange, 2004). This pattern also holds in the venture capital industry: VC
investments are geographically concentrated (Sorenson & Stuart, 2001), and 49% of all VC investments
are made to startups located in the Boston, New York and San Francisco metropolitan areas (Chen,
Gompers, Kovner, & Lerner, 2010). Startups located outside these VC clusters usually lack the face-toface interaction channels to build reputation and trust as startups located closer to investors do, and it is also
costly for investors to actually visit and inspect the startups located further away (Ivković & Weisbenner,
2005; Lerner, 1995; Massa & Simonov, 2006). Therefore, we expect to see social media playing a more
important role, where investors have fewer channels of information to look up for startups outside VC
clusters.
Hypothesis 6: A startup located further away from a VC cluster regions will experience astronger effect on their financing outcomes from social media activities.

Information Channel for Investor Evaluation

11

Beyond the search costs related to discovering investable startups, investors also have limited
information on which to evaluate their investments in new ventures, a problem exacerbated by anasymmetric information between investors and entrepreneurs where entrepreneurs have an incentive toover-represent their quality. Investors engage in a complex information acquisition process (due diligence)to screen and evaluate investment opportunities; the ability to evaluate deals is a key performancedifferentiator among early stage investors (Gompers, 1995; Van Nieuwerburgh & Veldkamp, 2009).
Prior studies show that activities on social media can reveal information on firm quality to investors
in online financial markets. Social media presence increases the success of crowdfunding activities(Agrawal et al., 2011). Similarly, more social media contacts (“friends”) on a peer-to-peer lending platform
increases the chance of reaching a funding target (Lin et al., 2013). In addition, early stage firms usually do
not have a fully functional product or service ready for sale yet, so social media success could reveal thepotential market size and customer reception for the product or service, thus foretelling a startup’s chanceof success. Anecdotal evidence also suggests that investors are increasingly evaluating metrics of social
media presence, such as the number of followers on Twitter or other metrics of social media reach—as wellas the sentiment of social media content (e.g. reviews, feedback) about a firm—when they make investment
decisions.6 An effective social media presence can serve as a signal of startup quality in an investor’s
evaluation process.
The role of social media as an additional startup quality information channel depends on theinvestor’s ability to process information. Experienced investors accumulate expertise and knowledge thatenable them to better process information and evaluate the quality of startup firms (Sørensen, 2007). These
investors should be more effective in analyzing the information from social media to evaluate startups and6Wall Street Journal. “If You Look Good on Twitter, VCs May Take Notice”. September 30, 2013.

http://www.wsj.com/articles/SB10001424127887324659404578499702279196058,

12

guide their investment decisions. Therefore, we expect that startups active on social media are more likely
to obtain investment from experienced investors.
Hypothesis 7: A startup active on social media is more likely to receive funding from experienced
investors.
On the other hand, the role of social media in providing quality information on startups will be
moderated by the existence of other information channels for the investors. One such channel is the networkof co-investors from past syndicated rounds. Venture capital firms and angel investors who jointly make
VC funding in a given startup are referred to as “syndicate partners”. These syndicate partners have asubstantial amount of interaction through the process of executing the round and then ex post throughadvising and monitoring the startup. Thus, prior syndicates reveal close collaborative relationship between
investors where information is shared (Hochberg et al., 2007). If investors have previous syndicate partners
who invested in a startup in an earlier round, they can acquire information on the startup quality from thesepartners, making alternative quality information sources like social media less important.
Hypothesis 8: A startup’s social media activities have less influence on investor with previous
syndicate partners already invested in the startup.

Data
Sample Construction
The main dataset consists of investment rounds into new technology-based ventures in 2007–2015
obtained from CrunchBase, combined with data on startup social media activities on Twitter from Twitter
API and Topsy.com.7 Crunchbase records information on startups, people affiliated with the startups andinvestors. It focuses specifically on the information technology sector and has been considered to be
representative of venture activity in their target markets (Block & Sandner, 2009; Wu, 2015). The

7

Crunchbase is operated by TechCrunch, an AOL Inc. subsidiary delivering news on the information technology
sector. Topsy.com is a certified Twitter partner, and maintains an archive of Twitter activity dating back to time
Twitter was established (2006).
13

Crunchbase data archive is obtained from a combination of user input and regulatory filings which are thenreviewed for accuracy and compiled by TechCrunch staff.

For each startup, there is data on thecharacteristics of each funding round to date (date, amount raised, type of funding, investor), characteristicsof the venture itself (founding date, employee count, type of business), and characteristics of the founders(prior venture experience and prior management experience).
We utilize Twitter as the source of social media data since it is the social media platform mostextensively used by startups and investors, and broadly used by the business community; 60% of startups
in our sample use Twitter while only 47% use Facebook and 36% used LinkedIn. The Twitter adoption
rate for startups across different business categories are shown in Figure 3.1. We observe substantive
Twitter usage by startups in different lines of business, with higher Twitter adoption rate in the news, media
and information related industries, and lower adoption by transportation and manufacturing related
businesses, as one would assume.
Some firms were excluded because the screenname utilized common English words (e.g., “path”,“square”, “tune”) which contaminate the data construction process on Twitter and Topsy.com, which rely
on a text search of the firms’ screenname.
We focus specifically on the 2nd round of VC financing for three reasons. First, we do not want to
use the 1st round of financing because not all startups are raising money, and we would not be able toempirically distinguish between those not raising money (“bootstrapping”) and those who are. Once a
startup closes a 1st round of funding, it reveals that the firm is not bootstrapping, and consistent with thepath of most technology startups backed by equity financing, they likely to need additional rounds offunding to sustain the firm. Second, we want to focus on earlier rounds of financing where public andprivate information available to investors is low and our theorized roles for social media still matters in
reducing search costs and serving as a quality signal. In the later funding rounds, the theorized role of social
media as an information channel would be harder to detect since there the startup firm has a track record

14already. Combining these first two points, the 2nd round is obviously the earliest round that isn’t the 1stround. Third, using the 2nd round allows us to use the 1st round as a control for firm size and quality.
Our data is primarily collected prior to the recent SEC regulation change in June 2015. In ourobservation window, startup firms have restrictions on the content they post on social media, specificallylimiting the announcement of investment information to the public. We expect to see that after the
regulation change, startup firms will more actively use social media to reach out to investors, and but exact
empirical effect of social media on financing outcomes remains an open empirical question for future
research.

Social Media Variables
We identify the Twitter page and screenname for each firm’s corporate account (if it exists), andthen use the Twitter API and Topsy.com API to gather information on Twitter activity, including:-- Number of tweets posted: the number of distinct Tweets for each screenname that contain alink;8-- Mentions: the number of distinct social media posts (tweets or links) that mention a startup’s
Twitter screenname in each month;-- Impressions: the number of potential views of a firm’s Tweets in each month;9-- Sentiment: a normalized score from 0 (most negative) to 100 (most positive) based on thesentiment of all tweets mentioning a firm’s screenname in each month;10-- Number of followers: a count of the number of Twitter followers for each screenname.11

8 Drawn from the Topsy.com archive, we utilize this proxy in lieu of the raw number of Tweets due to data limitations.
9 The Impressions variable is provided by the Topsy.com API, calculated by multiplying the number of tweets mentioning thestartup’s by the number of followers during each month of our sample period.
10 Sentiment was provided by the Topsy.com API.
11 The number of followers was constructed for our dataset by taking a snapshot at a single point in time, namely June 12th, 2015at 18:00.

15

We include 3 measures of Twitter activity: 1) whether the firm created its Twitter account prior toreceiving the 2nd round funding (Started Using Twitter), 2) the total number of Tweets posted in the 12month-window prior to receiving the 2nd round funding (Number of Tweets), 3) the first principal
component of the number of mentions, impressions, followers, and sentiment in tweets mentioning theTwitter account of the startup (Twitter Influence).

Dependent Variables
We focus on funding outcomes as the dependent variable (closing a 2nd round of funding, thenumber of investors participating in the 2nd round, and the size of the 2nd round funding) rather than otherkinds of startup or VC performance measures (e.g. startups’ successful exit; investors’ returns toinvestment) because we are currently concerned with the link between social media and financial markets
for early stage capital, although looking at other performance measures would be an excellent avenue for
future research. The size of a funding round is a good general measure of fundraising outcomes, and sincelarger amounts raised are correlated with larger valuation, it also provides some insight on the investor’s
expectation of the startup’s profitability and growth.

Control Variables
We include controls for startup characteristics (age, employee count, and number of different linesof business, other funding received prior to 1st round VC funding), founder experience (prior startup
experience, prior executive-level management experience), industry (industry indicators), and year received
the 2nd round funding (year indicators). The time and industry controls address market-wide conditions that
could potentially affect funding. Overall, these variables control for variation in startup quality and areconsistent with the prior literature on entrepreneurial financing (Hsu 2007).
To isolate the effects of Twitter from general online presence or other social media, we include
controls for web site traffic rank (Alexa rank of a firm’s homepage URL), search popularity (Google Trends
data for a firm name as a search term), and an indicator for the firm’s presence on Facebook. These

16

variables also control for other marketing activity and brand awareness in addition to directly measuring
online presence.
To control for communication from between prior investors in the startup to other investor throughtheir personal contacts, we include a measure of investors’ network connections through their syndicate
partners. We use the PageRank measure to capture how well-connected the investors are and their ability
to spread word about the startup to other investors; the PageRank measure captures the relative importanceof nodes by factoring in how many connections they have and how important these connections are (Brin& Page, 2012).
By including an extensive number of startup firm characteristics, including the size of the 1st roundof financing, we control for many sources of unobserved firm quality that could potentially confound ourestimates of social media’s effect on funding success. Furthermore, since many of these variables arelagging indicators (prior year firm characteristics) or measures of changes (e.g. a firm adopting Twitter),
we are less vulnerable to simultaneity between investment and social media use.

Summary Statistics
We report the summary statistics and correlation between main variables of interest in Table 1 andTable 2. Our data includes 2,880 startup firms, for 2nd round funding events across years 2007-2015. The
data is structured in a cross-sectional, with each startup firm appearing once. Social media measures arematched to the specific time-window before the 2nd round funding. Most of the other controls—such as firmage, website traffic, Google Trends measures, and founder controls—are matched to the specific timing of
the round as well. However, our measures of number of employees and the number of followers on Twitter,with are fixed based upon our time of data collection, and the year indicators should address the natural
time trend in these variables.

Empirical Methodology

17

To test our main empirical hypotheses, we estimate a basic regression model structured at theinvestment level. We further confirm the robustness of our findings against possible endogeneity stemmingfrom the omitted variable of startup quality with an instrumental variables analysis. We also present a panel
regression model in our appendix.

Main Analysis
After log-transforming the round size measures, we estimate the following ordinary least squares
model (with robust standard errors):log(2𝑛𝑑𝑅𝑜𝑢𝑛𝑑𝐹𝑢𝑛𝑑𝑖𝑛𝑔) = 𝛽0 log(1𝑠𝑡𝑅𝑜𝑢𝑛𝑑𝐹𝑢𝑛𝑑𝑖𝑛𝑔) + 𝛽1 𝐼𝑛𝑡𝑒𝑟𝑣𝑎𝑙+𝜷𝟐 𝑺𝒕𝒂𝒓𝒕𝒆𝒅 𝑼𝒔𝒊𝒏𝒈 𝑻𝒘𝒊𝒕𝒕𝒆𝒓 + 𝜷𝟑 𝑵𝒖𝒎𝒃𝒆𝒓𝒐𝒇𝑻𝒘𝒆𝒆𝒕𝒔 + 𝜷𝟒 𝑻𝒘𝒊𝒕𝒕𝒆𝒓 𝑰𝒏𝒇𝒍𝒖𝒆𝒏𝒄𝒆+𝛽5 𝑊𝑒𝑏𝑠𝑖𝑡𝑒𝑇𝑟𝑎𝑓𝑓𝑖𝑐𝑅𝑎𝑛𝑘 + 𝛽6 𝐺𝑜𝑜𝑔𝑙𝑒𝑇𝑟𝑒𝑛𝑑 + 𝛽7 𝑂𝑡ℎ𝑒𝑟𝑃𝑙𝑎𝑡𝑓𝑜𝑟𝑚+𝑠𝑡𝑎𝑟𝑡𝑢𝑝_𝑐𝑜𝑛𝑡𝑟𝑜𝑙𝑠 + 𝑓𝑜𝑢𝑛𝑑𝑒𝑟_𝑐𝑜𝑛𝑡𝑟𝑜𝑙𝑠 + 𝑏𝑢𝑠𝑖𝑛𝑒𝑠𝑠_𝑐𝑎𝑡𝑒𝑔𝑜𝑟𝑦 + 𝑦𝑒𝑎𝑟 + 𝜀
The model relates the amount raised in the 2nd round of financing to the amount raised in the 1st roundof financing, the time elapsed between rounds and the Twitter activity measures.

Instrumental Variables Analysis
One main endogeneity concern with the empirical analysis is that both social media activities andentrepreneurial financing could be influenced by the latent startup quality. In the main OLS analysis, we
control for some of this through the website traffic and Google Trend controls, measuring the generalpublic’s interest in the startup firms, accessing the startup homepages for product and service offerings or
searching for the relevant information. In addition, we seek to reduce the effect of this type of endogeneity
through the use of instrumental variables. Our identification strategy focuses specifically on the model
which utilizes funding outcomes as the dependent variable, since that model is most likely to be affectedby unobserved startup quality that might simultaneously influence social media influence. Using the same
instruments in the other models yields similar outcomes to the OLS results for these as well.

18

We use the following three sets of instrumental variables. First, we use social media activities ofother startups located in the same region. For each region and year combination, we look at the Twitter
presence, number of tweets posted and Twitter influence measures respectively for other startups locatedin the same region. Twitter usage for firms located in the same region is likely to be influenced by similar
factors, like the number of Twitter users in the region and users’ propensity to interact with startups online
but other firms’ social media activities should not directly influence the startup’s own funding outcomes.
Second, we use social media activities of other startups that their investors previously invested in. If
investors have different preferences of social media usage, this will lead to a correlation of social media use
among firms; however, since investment amounts depend on firms specific factors they are unlikely to becorrelated (especially since multiple investors tend to participate in the same investment round). Finally,
we use a geographic measure of the awareness and use of Twitter using Google trends data on the searchterm “twitter” from 2007-2015 in each state. If startups in this region are more active on Twitter, we expect
this to be reflected in the Google Trends, as consumers query for Twitter related information. This
instrument should be correlated with the social media metrics of the startups, but not be directly linked tostartup quality or funding outcomes.
Results from 2nd stage of 2SLS regression, using these three sets of IVs to instrument for startups’
starting the Twitter pages, number of tweets posted on Twitter and Twitter influence measures and using
2nd round funding as the dependent variable, are reported in accompany with each set of OLS results. We
do not find evidence of weak instrument problems based on the usual tests for first stage predictive power(F(65, 2694) = 38.11, p= 0.0001). Since the instruments help tease out the effect due to better quality
startups also more likely to be present on social media, we are able to better estimate the impact of social
media on startup funding. Results are largely consistent with what we observed before: presence on Twitter
improves startups’ amount of funding collected. This effect is mainly driven by Twitter influence rather
than Twitter activity. The economic size of the effects are comparable with the OLS estimates, for example,
columns (3) in Table 4 indicates that a one standard deviation increase in the Twitter Influence measure

19

leading to about $3.1 M increase in next period funding. The fact that our results are not weaker when using
2SLS estimates indicate that it is unlikely for our results to be entirely driven by endogeneity.

Results
Social Media Activities and General Funding Outcomes
To test our initial hypotheses that social media use is related to funding outcomes, we estimate
Equation (1) for the full set of startups for which we have complete data using ordinary least squares (OLS).
We first take a look at the overall influence of social media activities on startup funding outcomes, using
data on startups’ total amount of funding collected and the number of investors that they collect funding
from in the 2nd VC funding round. In Table 3, we report the results relating the log value of total funding
collected to the social media metrics and other control variables. The control variables all have signs in theright direction: startups who collected larger amount of funding in the 1st round, having more visits to theirwebpages (lower traffic rank) and attention from consumers (higher search volume as reported in Google
Trend for query of startups company names) are also likely to collect more funding in the 2nd round; shorter
interval between the two rounds are related to larger amount of 2nd round funding, as do startups withfounders that worked on more startup projects previously and with more executive management experience,but the effect sizes are smaller in these cases12.
Regarding the social media activity measures, we show that just being present on Twitter, without
active posting or engaging with users, does not lead to startups’ receiving larger amounts of funding(column 2, Table 3). This suggests that simply starting a Twitter page does not automatically gets the startup
firm more funding, which is consistent with prior studies that found that it is active use of social media thatgenerate desired outcomes for the firms (Miller & Tucker, 2013). We also observe that the number oftweets startups post on Twitter does not significantly influence its funding outcomes either. There appears

12

We control for startups presence on Facebook here, but do not control for LinkedIn presence, mostly because it is
not commonly used as a channel for information distribution for startups, but mostly used by employees to list theirplaces of employment and for recruitment purposes.
20

to be a mild quadratic relationship between the number of posts and funding outcome (column 4),
suggesting that posting more tweets initially benefit the startups, but posting too much content can actuallyhurt the firm (with the peak at about 290 tweets per year and when tweeting more than 580 tweets per year,
it could actually hurt the startup). The shape of the quadratic function is very flat though, indicating that ingeneral the effect size of number of tweets on funding outcomes is quite small. Startups can’t necessarily
benefit from social media simply by tweeting a lot. This is consistent with prior literature that mentioned
posting too much information could actually have a negative effect, most likely due to the cost of managing
tweets and lack of channel to really absorb the information collected from social media (Fischer & Reuber,2011). These results indicate that startups need to actively manage their content on social media, to better
engage other users, in order to see the benefits from social media. In fact, we see strong positive effect of
all the metrics relating to startups’ influence on Twitter. Specifically, getting mentioned more in otherpeople’s tweets, have more impressions of tweets, with more positive sentiments in others’ tweets
mentioning the startup firm and a larger follower-base can all improve startups’ funding outcomes. Since
these measures are correlated with one another and show consistent results, we take their 1st principal
component (Twitter Influence), to capture the overall impact (column 1, Table 4). We find that a one
standard deviation increase in the Twitter Influence measure leading to extra $1.5 million in 2nd roundfunding. This is equivalent with any of the following: 1) increase of number of mentions by 4.6%; 2)
increase of number of impressions by 12.0%; 3) Increase the number of followers by 209,815; 4) Increaseof average sentiment score in people’s tweets mentions of the startup firm by 0.02 (with all negative
sentiment as -1 and all positive sentiment as 1). Consistent results are obtained when we use the subsample
of startups who have started Twitter pages prior to receiving their 2nd round funding (column 2). Columns(3) and (4) report two stage least squares regression results. In column (5), we run the same regression using
Heckman selection model on a larger sample of all startup firms that received first round funding, andshowing that we get consistent results considering that we only observe 2nd round funding information forstartups that receive 2nd round funding. In column (6), we use the same sample as (5), and use the dummy
variable of whether the firm receives 2nd round funding as the dependent variable, to run a Logistic
21

regression, showing that both being present on Twitter and having stronger influence on Twitter increases
the likelihood for startups to receive 2nd round VC funding. These results support our first hypothesis thatsocial media activities improve startups’ funding outcomes.
Next, we look at whether startups’ activities on Twitter allow them to draw in a larger pool ofpotential investors. In Table 5, we use the total number of investors in the 2nd VC funding round as thedependent variable, and found that startups with more influential social media profiles are likely to get moreinvestors to make investments to them in the 2nd round VC funding (columns 1-2). Using alternative
regression frameworks, such as the negative binomial model (columns 3-4) and 2SLS (columns 5-6) show
consistent results. These results support our second hypothesis that social media activities help startup firms
get funded by a larger pool of investors.
The above results consistently demonstrate that startup firms’ social media activities influence theirfunding outcomes. Startups should be effective in their social media activities to build a positive brandimage, draw in a larger followers group, get more users to retweet their messages and have people leave
more positive feedbacks relating to their business. Startups that are more successful at generating influenceon social media see higher chances of continuing to receive funding, from a larger pool of investors andgetting larger amounts of funding overall.

Social Media Activities and Discovery of Investment Opportunities
We have demonstrated so far that startups’ social media activities contribute to funding success.
In the next step, we turn to investigate the mechanisms of social media’s influence on startup funding,through the discovery and evaluation of investment opportunities respectively. Firstly, we take a look at
how social media presence influences investors’ search for potential startup firms. We hypothesized thatfor investors with fewer channels of information to learn about potential investment opportunities, social
media’s function as a platform for broadcasting information is more important. We test for this byexamining the composition of investors participating in startups’ 2nd round funding, looking at the number22

of angel investors,13 while controlling for the total number of investors in the round.14 In columns (1) and(2) of Table 6, we show that startups present on Twitter are more likely to have a larger portion of angel
investors in the 2nd round15, whereas Twitter Influence have less influence here. Since we are controllingfor existing investors spreading word out about this startup by the PageRank measure of existing investors’
VC syndicate connections, the result that more angel investors joining in the 2 nd round for startups with
Twitter accounts is most likely due to investors’ discovery of new investment opportunities through social
media. Consistent results are observed if we only look at the number of angel investors who newly joinedin the 2nd round and did not participate in the first round funding, therefore making the discovery andinformation channel more salient (columns 3 and 4) and using 2SLS regressions (columns 5 and 6).
Investors’ own experience from previous investments and particularly investments in certain
industries also build up connections that investors can refer to in order to learn about new investment
opportunities. Therefore, we expect to see social media as play a larger role in discovering startups forinvestors with more diverse investment portfolios. On the other hand, for investors making concentrated
investments in certain industries and have consequently accumulated channels of information to learn about
new investment opportunities, we expect to see social media playing a smaller role. To measure the diversity
in investors’ investment portfolios, we look at the investors’ previous investments in other startups and thebusiness categories they belong to. We define investors ranked in the upper 25th percentile of number ofcategories covered in previous investments as investors making diverse investments 16 . Investors withindustry focuses as defined as those with total number of business categories covered in previous
investments ranking in the lower 25th percentile.

13

Since detailed data on amount of funding in dollar amounts contributed by each investor is unavailable and generally rare,
therefore, we only look at angel investors count and share.
14 While angel investors generally invest in earlier stages of startups’ development; it is not uncommon for angel investors toparticipate in the VC funding rounds as well.
15
We are using the number of angel investors participating in the 2 nd round funding here. Results are consistent if we use thenumber of all angel investors have not participated in previous rounds and only newly joined in the 2 nd round; similar for theinvestors with diverse portfolios and investors with industry focuses. Also, we get consistent results if we put the percentage ofangel investors in the 2nd round as the dependent variable.
16 Similar results if we use the Herfindahl-Hirschman Index of previous investments across different categories to define diversityof investors’ portfolio.

23

Columns (1)-(4) in Table 7 show that startups active on social media are more likely to get moreinvestors interested in making diversified investments to participate in the 2 nd round funding. In contrast,columns (5) -(8) show that startups active on social media generally have a lower ratio of investors making
investments in specific industries. Together, these two piece of evidence suggest that for investors investingin a business category they are familiar with, having sufficient connections with other investors andentrepreneurs to hear about new investment opportunities, social media’s role of broadcasting information
about startups and potential investment opportunities is less salient. On the other hand, for investors
interested in making investments across multiple business categories, who are less likely to be master in all
the categories, social media can be an effective channel of learning about startups in different lines ofbusiness and expanding the potential pool of investment opportunities.
Next, we look at the funding outcomes for startups located outside VC clusters, i.e. outside the
Boston, New York and San Francisco regions. These startups are located further away from investors andgeographic distances can potentially exacerbate the search cost and difficulty in obtaining information on
startups. In Table 8 we use the dummy variable (Far from VC) to indicate startup location outside VC
clusters and include its interaction terms with the social media activity measures. Results show that whilestartups located outside the VC clusters in general receives less funding than startups located inside the VC
cluster regions, they see additional gains in funding size from Twitter Influence, with one standard deviation
increase in Twitter Influence metrics adding $1.5 million more funding, compared with startups locatedinside VC clusters (column 2). These findings suggest that for startups located further from VCs, where
investors incur higher cost to obtain information, social media could present an additional information
channel.
Overall, results support our hypotheses 3-6, showing that social media facilitates theentrepreneurial financing process, by providing information about startups, reducing the search cost andencouraging investors to explore a wider pool of startup firms, especially for investors with fewer channels

24

of information, and for investors looking to make investments across different business categories but lackthe industry connections to know about potential investment opportunities otherwise.

Social Media as Additional Information Channel for Startup
Once investors have identified the potential startup firms, the next step is for them to evaluate theinvestment opportunity and decide whether to actually fund each startup. We hypothesize that social media
helps investors with this process, by providing more information about startup quality. For example, fromstartups’ social media profiles, investors can learn about the startup’s ability to build brand names throughthe online channel, reach out to target client groups, and also about consumers’ feedback on the startups’
products and services. Such additional information can help investors better evaluate the quality of thestartup firms and make their investment decisions.
As supporting evidence for the role of social media in conveying information about startup quality,
we look at startups’ ability to reach out to the experienced investors. Hypothetically, if social media only
works through the channel of discovering more investment opportunities, then startups should attract more
average investors and more experienced investors in similar patterns, with their active social media
presence. However, if the information on social media provide useful information on startup quality, thenthe experienced investors are more likely to effectively use the information in making their financing
decisions. We examine this mechanism in Table 9, taking a look at how social media activities influence
the number of experienced investors in the 2nd round funding, i.e. those who has made more than 100
investments up to date (which is the 80th percentile of investments made up to date for all investors17), while
controlling for total number of investors in the round. Results are consistent with our hypothesis, showingthat startups with more influence on Twitter get a higher portion of experienced investors (column 1),
similarly if we look at the subsample of Twitter users only (column 2). These results suggest that startups
more active on Twitter disproportionally attracts more experienced investors to invest in them, most likely

17

Other thresholds to identify experienced investors show consistent results.
25

because these investors are more capable in analyzing the information on social media to discover startup
quality and make investment decisions accordingly.
On the other hand, if the investors already have trusted channels of information to learn about thequality of the startups, we expect to see the role of social media as an information channel to be of lesssignificance. Specifically, we look at whether there are investors from previous funding rounds who arepartners with investors in the 2nd round in the same VC syndicates for other projects. If so, investors in the
2nd round can obtain credible information about this startup from these syndicate partner investors and rely
less on information from social media to deduce the quality about the startups. Evidence supports thishypothesis: in columns (3) and (4), we control for the percentage of investors in the 2nd round with partners
from previous VC syndicates already invested in the same startup firm (VC Syndicate), and include its
interactions with the social media measures. We observe that when a larger portion of the investors havealternative channels of learning about startup quality from previous syndicate partners, the effect of social
media in presenting quality signal for startups and attracting experienced investors to join in is lesssignificant. These results support our hypotheses 7 and 8, showing that social media not only act as achannel of broadcasting information about startups and letting investors discover the startups, but also
provides investors with another information channel to learn about startups’ quality and helping with theirevaluation process.

Conclusion
We find that startup firms active on social media have higher chances of getting funded, receive
larger amounts of funding, and have a larger number of investors—all consistent with the idea that social
media provides information that facilitates venture funding. These effects are attributed to social media
influence rather than simply started using social media. We further find these effects are larger for investors
that might lack channels for discovering investments (angels, diversified investors), and that funding
outcomes are improved in conditions where there is likely to be significant information asymmetry(ventures located outside VC clusters, investors lacking social network ties to get information about a26

startup). Thus, the gains associated with social media appear to be attributable to both an awareness effect,
where investors can learn about a larger number of potential investments, and an uncertainty reduction
effect, where uncertainty about quality is reduced in settings where alternative quality signals are lesseffective. These results are robust to various econometric methods (controls, instrumental variables) for
accounting for the problems related to unobserved variation in startup quality.
Our results highlight the importance for early stage ventures to establish a presence on social media,
especially where social media success can provide an indicator of their ability to attract and retain
customers. However, even firms that are not in consumer-facing industries can still benefit from expanding
awareness among investors. Given that our data is primarily in a period when there were restrictions onsocial media activity that limited investment-related communications, recent legislative changes that now
allow for greater information sharing on social media will likely increase the effect of social media on
funding success. Our results also imply that while “cheap talk” in the form of Twitter posts does not havemuch influence on funding as would be expected, the ability to effectively engage readers in social media(influence) does matter, suggesting benefits of even modest improvements in information availability insettings where there is considerable information asymmetry. While the use of extensive startup and social
controls contrasts within the data, and instrumental variables for addressing unobserved heterogeneity instartup quality does suggest the possibility that these effects are causal, in future work we hope to explore
the specific communications more directly to gain a better understanding of how this information iscommunicated by looking at the specific content of social media interaction. Overall, we hope that thisstudy and future related studies contribute to a better understanding of how the entrepreneurial financing
market is changing due to social media and what startup firms should do to take advantage of the newopportunities that come with it.

27

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Figures & Tables
Figure 1. Twitter Adoption Rate across Startup Business Categories

News
Social MediaE-Commerce
MultiMedia
Education
Analytics
LeisureHospitality
MobileAdvertisingEnterprise Software
Internet
General Service
Games
Consulting
Transportation
Manufacturing
50%

60%
70%
Percentage of Twitter Users

80%

Notes: 1. This graph shows the percentage of Twitter users for startups in different business categories; startups
who has started a Twitter page by the time of our sample collection (June, 2015) are counted as Twitterusers.

31

Table 1. Summary Statistics
Funding Round
2nd round funding
1st round funding
Months between 1st and 2nd rounds
Year received 2nd round funding
Number of Investors in 2nd RoundFirm Controls
Website Traffic Rank
Google Trends
Startup Age
Number of Business Categories
Employee Count
Existing Investors’ Page Rank
Founder Controls
Founders’ Previous Projects
Founders’ C-Level Experience
Twitter Measures
Started Using Twitter
Number of Followers
Number of Tweets
Twitter Mentions
Sentiment
Impressions
Twitter Influence

Obs.
Mean
Std. Dev.
Min
Max
2,880 15,100,000 21,600,000 50,000 542,000,000
2,880 6,994,867 8,027,593 48,268 124,000,000
2,880
18.94
10.65
0
95
2,880
2011.07
2.63
2007
2015
2,880
3.491
2.422
1
29
2,880
2,880
2,880
2,880
2,880
2,880

2,550,414
6.76
3.60
2.58
1,451.358
01,034,335
13.30
2.04
1.99
10,620.75
1

444
0
0
1
1
-1.18

3,267,739
78.5
10
14
87673
3.305

2,880
2,880

1.604
0.89

0.88
0.78

1
0

17
17

2,880
2,880
2,880
2,880
2,880
2,880
2,880

0.55
13,763.51
265.15
4,262.14
37.39
160,162.4
0

0.497
97,573.78
747.3029,334.13
27.24
746,651.4
1

0
0
0
0
0
0
-0.18412,439,962
12,914764,17199
9,397,203
22.90

32

Table 2. Correlations between Main Variablesnd

1. 2 round funding
2. 1st round funding
3. Months between 1st and 2nd rounds
4. Website Traffic Rank
5. Google Trends
6. Started Using Twitter
7. Number of Tweets
8. Twitter Influence

(1)
1.00
0.34
-0.01
0.01
0.04
0.01
0.00
0.01

(2)

(3)

(4)

(5)

(6)

(7)

(8)

1.00
0.02
0.04
0.01
-0.03
0.01
0.00

1.00
0.06
-0.08
-0.05
0.00
-0.03

1.00
-0.13
-0.09
-0.08
-0.04

1.00
0.13
0.11
0.16

1.00
0.32
0.17

1.00
0.20

1.00

33

Table 3. Startup Social Media Activities and Funding
DV: log(2nd round funding)
Label

(1)
Base

(2)
Started(3)
Tweets(4)
Tweets^2(5)
Mention(6)
Impressions(7)
Sentiment(8)
Followers(9)All

log(1st round funding)

0.549***
0.549***
0.549***
0.549***
0.547***
0.547***
0.550***
0.549***
0.546***(0.0261)(0.0261)(0.0261)(0.0261)(0.0261)(0.0259)(0.0261)(0.0261)(0.0259)
Interval between Rounds-0.00216-0.00216-0.00214-0.00213-0.00218-0.00193-0.00218-0.00214-0.00183(0.00163)(0.00163)(0.00163)(0.00163)(0.00163)(0.00161)(0.00163)(0.00164)(0.00161)
Website Traffic Rank
-0.00725-0.00730-0.00715-0.00402-0.00207-0.00283-0.00245-0.00687
0.00265(0.0264)(0.0264)(0.0264)(0.0156)(0.0264)(0.0261)(0.0264)(0.0264)(0.0153)
Google Trends
0.00333*** 0.00335*** 0.00341*** 0.00337*** 0.00290*** 0.00335*** 0.00305*** 0.00324*** 0.00318***(0.00104)(0.00104)(0.00104)(0.00104)(0.00104)(0.00104)(0.00105)(0.00104)(0.00105)
Has Facebook Page
0.0865*** 0.0901*** 0.0911*** 0.0882***0.0799**0.0723**0.0847**0.0897***0.0699**(0.0334)(0.0340)(0.0341)(0.0340)(0.0342)(0.0341)(0.0341)(0.0341)(0.0342)
Existing Investors' Page Rank 0.267***
0.266***
0.268***
0.270***
0.286***
0.270***
0.265**
0.267***
0.288***(0.0978)(0.0989)(0.0984)(0.101)(0.0979)(0.102)(0.106)(0.0999)(0.0959)
Started Using Twitter-0.0174-0.0154-0.0347-0.0102-0.131***-0.0516-0.0146-0.111**(0.0426)(0.0427)(0.0437)(0.0426)(0.0458)(0.0444)(0.0425)(0.0471)
Number of Tweets-0.00264
0.0698**0.0180(0.00406)(0.0303)(0.0302)
Number of Tweets^2-0.0897***-0.0627**(0.0336)(0.0302)
Twitter Mention
0.0870***0.0582**(0.0209)(0.0227)
Impressions
0.132***
0.124***(0.0228)(0.0262)
Sentiment
0.0492***-0.0104(0.0173)(0.0195)
Number of Followers
0.180*
0.117(0.0922)(0.0992)
Other Controls
Year Indicators, Business Category, Firm Age, Employee Count, Founder Experience, Other Prior Funding
Observations
R2

2,880
0.292

2,880
0.292

2,880
0.292

2,880
0.292

2,880
0.296

2,880
0.300

2,880
0.294

Notes: 1. Dependent Variable is the log value of funding collected in the 2nd round of VC funding;
2. Standard errors reported; *** p<0.01, ** p<0.05, * p<0.1

34

2,880
0.292

2,880
0.302

Table 4. Startup Social Media Activities and Funding Outcomes
DV: Log(2nd RoundFunding)
MethodSamplelog(1st RoundFunding)
Interval between
RoundsExisting Investors’
PageRank
Started Using Twitter
Twitter Influence

(1)

(2)

(3)

(4)

(5)

(6)

All

TwitterAll

TwitterLogit
w/ 1st R.

Heckmanw/ 1st R.

0.547***

0.536***

0.547***

0.525***

0.144***

0.564***(0.0260)(0.0282)-0.00206 -0.00544***(0.00162)(0.00209)
0.285***
0.354***(0.103)(0.0645)-0.0860*(0.0443)
0.0993*** 0.0948***(0.0186)(0.0208)(0.0232)-0.00175(0.00152)
0.348(0.230)0.255(0.513)0.206***(0.0484)(0.0265)-0.00500***(0.00181)
0.396*(0.217)(0.0557)(0.0204)

OLS

2SLS

0.212***(0.0409)

LambdaOther Controls
Observations
R2

0.170*** 0.380**(0.0326)(0.190)0.591***
0.0503(0.0720) (0.0391)
0.181*** 0.0619***(0.0361) (0.0176)-0.0620(0.120)Year Indicators, Business Category, Firm Age, Employee Count, Founder Experience,
Website Traffic Rank, Google Trends, Facebook Presence, Other Prior Funding

2,880
0.298

1,588
0.364

2,762
0.265

1,527
0.345

6,378
0.730

6,378

Notes: 1. In columns (1) and (2), the dependent variable is the log of 2 nd Round Funding; columns (1) and (2) reportresults from OLS regression; columns (3) and (4) report results using two stage least squares. Columns (5) and(6) looks at the sample of all startups that received 1 st round funding. In Column (5), the dependent variable isdummy variable for whether or not the startup firm receives 2 nd round VC funding, reporting results fromlogistic regression; Column (6) uses Heckman model, taken into consideration that startups not receiving 2 ndround VC founding would not have the funding amount available.
2. Three sets of instruments in the 2SLS regression: 1) Google Trend for the keyword “Twitter” in each region;2) Twitter usage, number of tweets and twitter influence in the other startups located in the same region; 3)
twitter usage, number of tweets and twitter influence in other firms that the investors previously invested in.
3. Columns (1)(3) use all startups that received 2nd round funding, columns (2)(4) use the subsample of startup
that has started Twitter page before receiving 2nd round VC funding, columns (5)(6) use all startups thatreceived 1st round funding
4. Standard errors reported; *** p<0.01, ** p<0.05, * p<0.1

35

Table 5. Social Media and Number of Investors in 2nd Round VC Funding
DV: Number of Investors
MethodSampleLog(1st Round Funding)Interval between Rounds
Existing Investors’
PageRank
Started Using Twitter
Twitter Influence
Other Controls
Observations
R2

(1)

(2)
OLS

All

Twitter

0.328***(0.0648)-0.00608(0.00420)-0.388(0.325)-0.317***(0.117)0.244***(0.0631)0.245***(0.0940)-0.00702(0.00592)-0.144(0.378)0.178**(0.0717)(3)(4)
Negative Binomial
All
Twitter
0.0919***(0.0325)-0.00164(0.00227)-0.150(0.385)-0.0877(0.0671)
0.0713**(0.0293)

0.0669(0.0433)-0.00227(0.00301)-0.0734(0.408)0.0474(0.0333)

(5)

(6)
2SLS
All
Twitter
0.492***(0.119)-0.00163(0.00780)
0.0674(1.180)9.284***(2.659)0.0889(0.249)0.253***(0.0914)-0.00313(0.00626)-0.0592(0.750)0.543***(0.141)

Year Indicators, Business Category, Firm Age, Employee Count, Other Funding,Founder Experience, Website Traffic Rank, Google Trends, Facebook Presence

2,880
0.125

1,588
0.126

2,880

1,588

2,762

1,527
0.109

Notes: 1. The dependent variable is the number of investors in the 2 nd round; columns (1) and (2) report results from
OLS regression; columns (3) and (4) report results using Negative Binomial Model; columns (5) and (6) reportresults using two stage least squares.
2. Columns (1)(3)(5) use all startups and columns (2)(4)(6) use the subsample of startup that has started Twitter
page before receiving 2nd round VC funding
3. Standard errors reported; *** p<0.01, ** p<0.05, * p<0.1

36

Table 6. Social Media and Search Cost Reduction – Angel Investors
Dependent Variables
MethodSample(1)(2)
Number of Angel
Investors in 2nd Round
OLS
All
TwitterNumber of Investors in
2nd Roundlog(1st Round Funding)

0.218***(0.0238)-0.104***(0.0226)
Interval between Rounds 0.000941(0.00146)
Started Using Twitter
0.0851**(0.0386)
Twitter Influence
0.0261(0.0196)(3)(4)
Number of New Angel
Investors in 2nd Round
OLS
All
Twitter(5)(6)
Number of Angel
Investors in 2nd Round
2SLSAll
Twitter

0.297*** 0.177***
0.242***
0.222*** 0.296***(0.0314)(0.0218)(0.0289)(0.00867) (0.00974)-0.128*** -0.0777*** -0.0947*** -0.0841*** -0.138***(0.0322)(0.0213)(0.0309)(0.0284)(0.0343)0.00406*
0.001180.00453**
0.00214 0.00479**(0.00224) (0.00137) (0.00215) (0.00192) (0.00235)
0.0836**1.770***(0.0341)(0.408)0.01740.01700.00894
0.105*
0.219***(0.0233)(0.0171)(0.0208)(0.0588)(0.0511)

Other Controls

Year Indicators, Business Category, Firm Age, Employee Count,
Other Prior Funding, Existing Investors' Page Rank, Founder Experience,
Website Traffic Rank, Google Trends, Facebook Presence

Observations
R2

2,880
0.342

1,588
0.443

2,880
0.306

1,588
0.401

2,762
0.233

1,527
0.376

Notes: 1. In columns (1)(2)(5)(6), the dependent variable is the number of angel investors in the 2nd funding round;in columns (3) and (4), the dependent variable is the number of angel investors in the 2nd funding round who
did not invest in the 1st round
2. Columns (1)-(4) report results from OLS regression; columns (5) and (6) report results using two stage leastsquares; columns (1)(3)(5) use all startups and columns (2)(4)(6) use the subsample of startup that has started
Twitter page before receiving 2nd round VC funding
3. *** p<0.01, ** p<0.05, * p<0.1

37

Table 7. Social Media and Search Cost Reduction – Investors’ Investment Diversity(1)

Dependent
Variables
MethodSampleNumber ofInvestors in 2nd
Roundlog(1st RoundFunding)
Interval
betweenRoundsStarted Using
Twitter
Twitter
Influence
Other Controls

Observations
R2

(2)
(3)
(4)
Number of Investors
with Diverse Portfolios
OLS
2SLS
All
TwitterAll
Twitter

(5)

(6)
(7)
(8)
Number of Investors
with Industry Focus
OLS
2SLS
All
TwitterAll
Twitter

0.245***

0.279***

0.227***

0.270***

0.180***

0.0926*** 0.181*** 0.0948***(0.0157)
0.237***(0.0227)0.304***(0.0105)
0.209***(0.0119)0.298***(0.0177)
0.0430*(0.0133)
0.0333(0.00796) (0.00720)
0.03930.0346(0.0285)(0.0414)(0.0342)(0.0418)
0.0078*** 0.0083*** 0.0079*** 0.0084***(0.0261)
9.79e-05(0.0280)-0.00132(0.0260)
0.000296(0.0019)0.0466(0.00171)-0.137***(0.00170) (0.00177) (0.00173)-0.930**(0.0581)0.177***(0.0027)(0.0023)-1.752***(0.0497)(0.375)-0.123** -0.0828**
0.0702*** 0.0531***(0.0271)(0.0309)(0.0712)(0.0625)(0.0188)(0.0181) (0.0542) (0.0379)
Year Indicators, Business Category, Firm Age, Employee Count,
Other Prior Funding, Existing Investors' Page Rank, Founder Experience,
Website Traffic Rank, Google Trends, Facebook Presence
2,880
1,588
2,762
1,527
2,880
1,588
2,762
1,527
0.439
0.468
0.240
0.451
0.443
0.291
0.384
0.300
0.164***(0.493)0.588***(0.0029)(0.0254)-0.00116

0.317***

Notes: 1. In columns (1)-(4), the dependent variable is the number of investors with diverse investment portfolios(i.e. in the upper 25th percentile of total number of business categories covered in previous investments); incolumns (5)-(8) the dependent variable is the number of investors with industry focuses (i.e. invested in thebusiness category before and in the lower 25th percentile of total number of business categories covered byprevious investments)
2. Columns (1)(2)(5)(6) report results from OLS regression; columns (3)(4)(7)(8) report results from 2SLS
regression; columns (1)(3)(5)(7)use all startups and columns (2)(4)(6)(8) use the subsample of startup that has
started Twitter page before receiving 2nd round VC funding
3. Standard errors reported; *** p<0.01, ** p<0.05, * p<0.1

38

Table 8. Social Media and Search Cost Reduction – Startup Location
DV: Log(2nd Round Funding)MethodSamplelog(1st Round Funding)Interval between Rounds
Existing Investors’ PageRank
Started Using Twitter
Twitter Influence
Far from VC
Far from VC * Started Using
Twitter
Far from VC * Twitter Influence
Other Controls

Observations
R2

(1)
OLS
All

(2)
OLS
All

(3)
OLS
Twitter

(4)
2SLS
All

(5)
2SLS
Twitter

0.536***(0.0259)-0.00193(0.00162)
0.299***(0.106)-0.0871**(0.0439)0.0915***(0.0186)

0.536***
0.520***(0.0260)(0.0284)-0.00188 -0.00522**(0.00160) (0.00207)
0.302***
0.364***(0.109)(0.0812)-0.00632(0.0517)0.0512***0.0413*(0.0197)(0.0217)0.528***(0.0222)-0.00165(0.00148)
0.336(0.225)-0.00222(0.368)0.0759(0.0575)

0.511***(0.0260)-0.00485***(0.00177)
0.400*(0.212)-0.172***(0.0295)-0.0803(0.0515)-0.162**(0.0720)0.102***(0.0372)

0.112(0.0903)-0.494***(0.153)0.215***(0.0737)-0.297***(0.0462)-0.269***(0.0429)0.122***(0.0392)

0.0745*(0.0450)

0.190***(0.0555)

Year Indicator, Business Category, Firm Age, Employee Count,
Founder Experience, Website Traffic Rank, Google Trends,
Facebook Presence, Other Prior Funding
2,880
2,880
1,588
2,762
1,527
0.307
0.309
0.382
0.297
0.375

Notes: 1. Far from VC is a binary variable indicating whether the startup firm is located within the VC cluster regionsof Boston, New York and San Francisco.
2. Columns (1)(2)(4) uses all the sample and column (3)(5) uses the subsample of startup that has started
Twitter page before receiving 2nd Round Funding; Columns (1)-(3) report results from OLS regression;columns (4) and (5) report results from 2SLS
3. *** p<0.01, ** p<0.05, * p<0.1

39

Table 9. Social Media and Quality Signal – VC Syndicates
DV: Number of Experience
Investors
Samplelog(1st Round Funding)Interval between Rounds
Number of Investors
Started Using Twitter
Twitter Influence

(1)

(2)

(3)

(4)

All

Twitter Users

All

Twitter Users

0.183***(0.0211)-0.00469***(0.00137)
0.110***(0.0102)-0.00359(0.0393)0.145***(0.0205)

0.258***(0.0327)-0.00539***(0.00191)
0.125***(0.0153)0.181***(0.0211)-0.00444***(0.00137)
0.108***(0.0104)
0.000800(0.0395)0.140***(0.0201)-0.0146(0.0114)-0.0537***(0.0162)-0.0217**(0.0103)0.254***(0.0327)-0.00503***(0.00191)
0.123***(0.0154)0.129***(0.0243)

VC Syndicate
VC Syndicate * Started Using
Twitter
VC Syndicate * Twitter
Influence
Other Controls
Observations
R2

0.126***(0.0239)-0.0530***(0.0142)-0.0190(0.0124)

Year Indicator, Business Category, Other Prior Funding, Firm Age,
Employee Count, Founder Experience, Website Traffic Rank,
Google Trends, Facebook Presence, Existing Investors’ PageRank

2,880
0.347

1,588
0.350

2,880
0.350

1,588
0.353

Notes: 1. The dependent variable is the number of experienced investors in the 2nd round, defined as those having
made more than 100 investments up to date (top 20 percentile in number of investments made to date)
2. VC Syndicate is the percentage of investors in the 2nd round with partners from previous VC syndicatesalready invested in the same startup firm;
3. Columns (1) and (3) use all the sample and columns (2) and (4) use the sample of startups with Twitter pageprior to receiving 2nd Round Funding;
4. Standard errors reported; *** p<0.01, ** p<0.05, * p<0.1

40Appendix
Panel Regression with Fixed Effects
Another approach to control for the unobserved startup quality is to use a panel structure setup, withobservations for startup-year combinations and calculating the total amount of funding the startup firms hascollected up to date:log(𝑇𝑜𝑡𝑎𝑙𝐹𝑢𝑛𝑑𝑖𝑛𝑔) = 𝛼0 + 𝛽0 𝐹𝑖𝑟𝑚𝐴𝑔𝑒 + 𝛽1 𝑺𝒕𝒂𝒓𝒕𝒆𝒅 𝑼𝒔𝒊𝒏𝒈 𝑻𝒘𝒊𝒕𝒕𝒆𝒓+𝜷𝟐 𝑵𝒖𝒎𝒃𝒆𝒓𝒐𝒇𝑻𝒘𝒆𝒆𝒕𝒔 + 𝜷𝟑 𝑻𝒘𝒊𝒕𝒕𝒆𝒓 𝑰𝒏𝒇𝒍𝒖𝒆𝒏𝒄𝒆+𝛽5 𝑊𝑒𝑏𝑠𝑖𝑡𝑒𝑇𝑟𝑎𝑓𝑓𝑖𝑐𝑅𝑎𝑛𝑘 + 𝛽6 𝐺𝑜𝑜𝑔𝑙𝑒𝑇𝑟𝑒𝑛𝑑 + 𝛽7 𝑂𝑡ℎ𝑒𝑟𝑃𝑙𝑎𝑡𝑓𝑜𝑟𝑚+𝑏𝑢𝑠𝑖𝑛𝑒𝑠𝑠_𝑐𝑎𝑡𝑒𝑔𝑜𝑟𝑦 + 𝑦𝑒𝑎𝑟 + 𝜀

In Table A.1, we relate the log of total funding collected up to date, to the social media activities
measures and the startup and entrepreneur level controls, including startup level fixed effects to capture
unobserved quality (Columns 1 and 2). Results are largely consistent with before, indicating that startups
present on social media, actively posting tweets and having high influence measure, are more likely tocollected more funding across the years. Compared with results in Table 4, in the Fixed Effects regressions,being present on Twitter and tweeting information also positively contributes to funding outcomes. This isprobably due to the accumulated effect over the years of heterogeneity across startup firms. In addition, we
instrument for the Twitter activity measures on top of the Fixed Effects model, we continue to observe thatstartups with stronger influence on Twitter are more likely to collect larger sums of funding in total(Columns 3 and 4). The directions of the effects are consistent with before, while the scales are slightly
higher compared with columns 1 and 2. This is likely due to the fact that we are already controlling forstartup fixed effects and having many control variables in place, the marginal effects captured by IVs could
be larger in scale. Still, the IV results indicate that we are not over-estimating the size of the effect.

41

Table A.1. Social Media Activities and Total Funding, Fixed Effects
DV: log(total funding)
Model
SampleFirm Age
Website Traffic Rank
Google Trends
Started Using Twitter
Number of Tweets
Twitter Influence
Other Controls
Constant
Observations
R-squared

(1)
FE
All

(2)
FE
Twitter Users

(3)
FE/IV
All

(4)
FE/IV
Twitter Users

0.321***(0.0143)-0.507***(0.0369)-0.00570(0.00375)
1.340***(0.0605)
0.0219***(0.00591)
0.766***(0.0307)0.404***(0.0175)-0.469***(0.0397)-0.00885**(0.00386)
1.683***(0.0673)
0.0218***(0.00589)
0.801***(0.0309)0.508***(0.0292)-0.286***(0.0731)-0.0164(0.0106)-5.869***(1.046)0.978***(0.297)2.100***(0.520)0.368***(0.0926)0.716(0.780)-0.0468**(0.0231)
17.26(21.86)2.348***(0.860)3.178*(1.768)Year Controls, Business Category, Dummies for missing Variables
11.36***(0.542)
105,292
0.738

10.39***(0.581)
74,2830.739

7.497***(1.019)104,834
18,054-9.711(13.25)74,011
13,257

Notes: 1. Dependent variable is the log of the total amount of funding collected up to date;
2. Columns (1) and (2) show results using fixed effects regression; columns (3) and (4) use fixed effects
regression with instrumental variables. The instruments are: 1) Google Trend for the keyword “Twitter” ineach region; 2) Twitter activities in the other startups located in the same region; 3) Twitter activities in otherfirms that the investors previously invested in.
3. Columns (1) and (3) uses all the sample and columns (2) and (4) use the subsample of startups that haveeventually started Twitter Page
4. Robust standard errors reported in parentheses, *** p<0.01, ** p<0.05, * p<0.142