Identifying Different Types of Social Ties in Events from Publicly

Available Social Media Data

Jayesh Prakash Gupta

1 a

, Hannu K

arkk

ainen

1 b

, Karan Menon

1 c

, Jukka Huhtam

aki

1 d

Raghava Rao Mukkamala

3,5 e

, Abid Hussain

3 f

, Ravi Vatrapu

5,6 g

, Jari Jussila

4 h

Henri Pirkkalainen

1 i

and Thomas Olsson

2 j

Unit of Information and Knowledge Management, Tampere University, Tampere, Finland

Faculty of Information Technology and Communication, Tampere University, Tampere, Finland

Department of Digitalization, Copenhagen Business School, Copenhagen, Denmark

HAMK Smart Research Unit, H

ame University of Applied Sciences, H

ameenlinna, Finland

Department of Technology, Kristiania University College, Oslo, Norway

Dept. of ITM, Ted Rogers School of Management, Ryerson University, Toronto, Canada

Keywords: Tie Strength, Weak Ties, Social Media, Twitter, Facebook.

Abstract:

Tie strength is an essential concept in identifying different kind of social ties - strong ties and weak ties. Most

present studies that evaluated tie strength from social media were carried out in a controlled environment and

used private/closed social media data. Even though social media has become a very important way of network-

ing in professional events, access to such private social media data in those events is almost impossible. There

is very limited research on how to facilitate networking between event participants and especially on how to

automate this networking aspect in events using social media. Tie strength evaluated using social media will be

key in automating this process of networking. To create such tie strength based event participant recommen-

dation systems and tools in the future, ﬁrst, we need to understand how to evaluate tie strength using publicly

available social media data. The purpose of this study is to evaluate tie strength from publicly available social

media data in the context of a professional event. Our case study environment is community managers’ online

discussions in social media (Twitter and Facebook) about the CMAD2016 event in Finland. In this work, we

analyzed social media data from that event to evaluate tie strength and compared the social media analysis-

based ﬁndings with the individuals’ perceptions of the actual tie strengths of the event participants using a

questionnaire. We present our ﬁndings and conclude with directions for future work.

1 INTRODUCTION

The concept of tie strength was originally proposed by

Mark Granovetter (1973) in his seminal study “The

Strength of Weak Ties”. According to Granovetter

there are two main kinds of social ties (strong ties and

https://orcid.org/0000-0003-4043-4818

https://orcid.org/0000-0003-4753-4416

https://orcid.org/0000-0001-9948-9659

https://orcid.org/0000-0003-2707-108X

https://orcid.org/0000-0001-9814-3883

https://orcid.org/0000-0002-8985-3020

https://orcid.org/0000-0002-9109-5281

https://orcid.org/0000-0002-7337-1211

https://orcid.org/0000-0002-5389-7363

https://orcid.org/0000-0002-1106-2544

weak ties) and tie strength evaluation can be used to

understand these different interpersonal relationships

or social ties. Over the decades the concept of tie

strength has been of signiﬁcant interest in academic

research in various different domains and has over

50000 citations on Google scholar.

The rise of social media has enabled new ways

to establish, strengthen and manage social ties on-

line (Ahn and Park, 2015). This has resulted in a lot

of studies that have used social media data to evalu-

ate tie strength and identify the different kind of so-

cial ties (e.g. (Gilbert and Karahalios, 2009; Fogu

et al., 2013; Ahn and Park, 2015). Most of these stud-

ies have either used explicit social media relationship

data (e.g. Friends in Facebook, Followers/Followee in

Twitter) or private social media data of study partic-

176

Gupta, J., Kärkkäinen, H., Menon, K., Huhtamäki, J., Mukkamala, R., Hussain, A., Vatrapu, R., Jussila, J., Pirkkalainen, H. and Olsson, T.

Identifying Different Types of Social Ties in Events from Publicly Available Social Media Data.

DOI: 10.5220/0008065501760186

In Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2019), pages 176-186

ISBN: 978-989-758-382-7

ipants and in many cases both of them (Gupta et al.,

2016). Some of the studies also collected the social

media data by crawling the social media pages of the

study participants in a controlled environment (e.g.

Gilbert and Karahalios (2009)). However, in the past

few years the social media platforms have become

very restrictive and allow data access only through

their application programming interface (API). Also,

a lot of data which was earlier accessible is no longer

accessible (e.g. Friends in Facebook) and practices

like data crawling are illegal on these social media

platforms. Along with this introduction of new data

protection laws like General Data Protection Regula-

tions (GDPR) by the European Union has further re-

stricted the use of private social media data. Hence,

there is a need to carry out research related to tie

strength which relies on publicly available social me-

dia data.

The use of social media for maintaining and es-

tablishing ties has gone beyond the private life and is

increasingly being used in a professional context like

conferences. One of the main purposes of such pro-

fessional events is to facilitate networking and ﬁnding

potential collaborations between event participants

(Ross et al., 2011). One increasingly important means

for networking people in such professional events like

conferences is social media (Reinhardt et al., 2009).

Many such professional events also use conference

recommendation tools and systems (e.g. Zhang et al.

(2016)) to facilitate networking. To the best of our

knowledge, the current conference related recommen-

dation tools and systems don’t use or incorporate the

tie strength aspect while making a recommendation.

Even recommendation systems in general rarely use

the aspect of tie strength (Zhong et al., 2015). Tie

strength matters in case of professional events like

professional conferences. Tie strength enables iden-

tifying different kind of social ties (strong, weak) i.e.

different kind of people. Incorporating tie strength

element into such a conference recommendation sys-

tem will enable providing a more useful and relevant

recommendation for the event participants. In gen-

eral and more speciﬁcally in the context of events it

is impossible to get either explicit online relationship

data or private social media data of event participants.

Thus, the previous tie strength related studies cannot

be used. However, it is possible to collect publicly

available social media data of the event. In order to

enable incorporation of tie strength aspect into the fu-

ture conference recommendation tools and systems,

we need to ﬁrst understand how to evaluate the tie

strength and identify different types of social ties from

publicly available social media data of an event.

The current literature does not provide any clear

methods for evaluating tie strength using publicly

available social media data in the context of an event.

Taking into consideration the above-described re-

search gaps in current literature, we have derived the

following research question to address the research

gaps:

RQ. How can tie strength be evaluated from pub-

licly available social media data in the context of

events ?

The structure of the paper is as follows. In sec-

tion 2, we ﬁrst introduce the concept of tie strength,

then tie strength evaluation using social media and

how networking is done in events. Then in section 3,

we provide the case description, data collection and

data analysis methods used in paper and section 4 will

present our ﬁndings. Finally, in section 5, we will

discuss the conclusions, managerial implications and

future work.

2 TIE STRENGTH IN AN EVENT

SETTING

In this section, we will brieﬂy present the concept of

tie strength, especially in the context of social media

data and networking events.

2.1 Concept of Tie Strength

Granovetter introduced the concept of tie strength

through the seminal paper titled ”Strength of weak

ties” (Granovetter, 1973). According to Grannovet-

ter, tie strength can be deﬁned as ”a (probably linear)

combination of the amount of time, the emotional in-

tensity, the intimacy (mutual conﬁding), and the re-

ciprocal services which characterize the tie”. Based

on this deﬁnition he characterized two kinds of social

ties - strong ties and weak ties. Strong ties are peo-

ple whom you trust and who can provide you emo-

tional support for example family members or close

friends. On the other hand, weak ties are people with

whom you just have acquaintance.(Granovetter, 1973;

Gilbert and Karahalios, 2009) Weak ties can serve as

bridges to diverse part of a persons’ social network

and can provide access to novel information (Mars-

den and Campbell, 1984, 2012).

In his original study, Granovetter theorized that

weak ties provided a novel source of information

while looking for a new job. Since the original study,

many studies have operationalized tie strength using

communication frequency as a proxy for tie strength

(Marsden and Campbell, 1984; Onnela et al., 2007;

Wiese et al., 2015). Over the decades, the concept

of tie strength has been used to study various social

Identifying Different Types of Social Ties in Events from Publicly Available Social Media Data

177

phenomena beyond the original job-seeking context.

(e.g. knowledge transfer (Levin and Cross, 2004) ,

information diffusion (Gilbert and Karahalios, 2009),

and content sharing (Aral and Walker, 2014; Zhan

Shi et al., 2014). At the same time, the tie strength

measurement has been extended from its original use

at an interpersonal level to organizational and inter-

organizational levels as well (Zhang et al., 2017). In

this exploratory study, we evaluate tie strength at in-

terpersonal level (between the event participants) and

use interaction frequency of the event participants on

the social media as a proxy for tie strength evaluation

in the context of a professional event.

2.2 Tie Strength using Social Media

The rise of social media has given rise to new ways

to establish and manage ties online (Ahn and Park,

2015) resulting in studies which use social media data

to calculate tie strength of these online relationships.

Table 1 provides a list of some of the most prominent

studies which have used social media data for evaluat-

ing tie strength and identifying different kind of social

ties. Table 1 also provides information about the kind

of social media used, also whether the social media

data used in the study was publicly available or was

private/ closed. In this study, publicly available so-

cial media data refers to the social media data that can

be directly accessed using the API of the social me-

dia platform (e.g. text from an open Facebook page,

tweet from Twitter). On the other hand private/ closed

social media data refers to data which is collected in

controlled environment from the participants of the

experiment/study (e.g. Gilbert and Karahalios (2009);

Fogu

es et al. (2013); Backstrom and Kleinberg (2013)

) and requires separate explicit user permissions (e.g.

Friend list in Facebook, direct messages in Twitter)

and cannot be directly accessed from the social media

API.

Most of the previous studies have used explicit re-

lationship and/or private social media data to calculate

the tie strength. Also, some of these studies have used

methods like data crawling which are no longer al-

lowed by social media. For example, studies to calcu-

late tie strength using Facebook have used data related

to participant’s Facebook proﬁle and friends. In the

case of Twitter, data about the participant’s followers

and followees has been used to calculate tie strength.

(Ahn and Park, 2015; Gilbert, 2012; Fogu

es et al.,

2013; Gilbert and Karahalios, 2009) Some studies

have used supervised computational methods that re-

quired human annotations like rating friends or nomi-

nating top friends (Kahanda and Neville, 2009; Xiang

et al., 2010). Some studies have used unsupervised

computational methods but have still used private so-

cial media data (Xiang et al., 2010).

It can be seen from Table 1 that there has been

one study which has utilized publicly available Twit-

ter data to evaluate tie strength in order to study the

phenomena of social broadcasting (see Zhan Shi et al.

(2014)). There has also been limited research on us-

ing the implicit relationships inferred from the pub-

licly available social media content (Tweets, Face-

book posts) (Huang et al., 2015; Gupta et al., 2016).

One such study focused on studying the phenomena

of triadic closure using implicit relationships (Huang

et al., 2015). However, these studies do not provide

measures which can be directly used for tie strength

evaluation in the context of events.

From Table 1 it can be seen that there is very lim-

ited research which utilizes only publicly available so-

cial media data for identifying a different kind of so-

cial ties using tie strength and is almost nonexistent

in the case of professional events. Also, the introduc-

tion of data protection laws like the GDPR has further

restricted the use of private social media data. Thus

there is a need to have studies which can use the pub-

licly available social media data for tie strength eval-

uation.

This study differs from and builds on earlier stud-

ies by making use of publicly available social media

data about an event. We draw data from two differ-

ent social media platforms and use only the implicit

relationships inferred from the content of the publicly

available social media of an event.

2.3 Networking in Events

In recent years, social media has provided a new

way of networking with other people even in co-

located professional events like professional confer-

ences (Zhang et al., 2016). In such conferences, one

of the aims of the participants is to meet new people

who may share similar interests or may provide rele-

vant information (Reinhardt et al., 2009). This need

has resulted in a desire to build conference recom-

mendation systems which may provide relevant rec-

ommendations for the participants about which par-

ticipants to meet (Hornick and Tamayo, 2012).

In general, these systems have relied on giving

a recommendation based on certain keywords which

are usually extracted from the event participant’s reg-

istration form or some other provided details (Zhong

et al., 2015; Hornick and Tamayo, 2012). Recently

some studies have tried to incorporate other sources of

data like bibliographic data, co-occurrence data, par-

ticipant’s mobile device data and also data from sites

like epinions.com, Flickr to provide more relevant

KMIS 2019 - 11th International Conference on Knowledge Management and Information Systems

178

Table 1: Tie strength calculation in social media using public and private/closed data.

Paper Social Media Used Type of Data Sets Used Context/ Area of Study

Facebook Twitter Other Public Private/Closed

Aral and Walker (2014) X X Social Inﬂuence on Consumer Demand

Backstrom and Kleinberg (2013) X X Finding romantic relationships

Fogu

es et al. (2013) X X Privacy Assistance

Gilbert and Karahalios (2009) X Tie strength evaluation

Gilbert (2012) X X Tie strength evaluation

Kahanda and Neville (2009) X X Link Prediction

Petroczi et al. (2007) X X Measure tie strength in virtual communities

Wegge et al. (2015) X X Cyberagression on Social Network Sites

Xiang et al. (2010) X X X X Modelling relationship strength

Zhan Shi et al. (2014) X X Social Broadcasting

Hossmann et al. (2012) X X X Opportunistic Networks

Arnaboldi et al. (2013) X X Tie strength prediction

Servia-Rodr

ıguez et al. (2014) X X Socially enhanced applications

Pappalardo et al. (2012) X X X X Tie strength in multidimensional social networks

Quijano-S

anchez et al. (2014) X X Group movie recommender application

L. Fogues et al. (2018) X X Tie strength for photo sharing

recommendations (Zhang et al., 2016; Zhong et al.,

2015; Gupte and Eliassi-Rad, 2012). Data from so-

cial media platforms like Twitter has been used by the

conference organizers to gain better insight into the

conference and help in better planning for future con-

ferences (Aramo-Immonen et al., 2015). However,

there is limited research on the use of tie strength-

based recommendation systems (Zhong et al., 2015)

in case of a professional event like a conference. The

unavailability of explicit online relationship data and

private social media data restricts the use of previous

social media data based tie strength studies in the con-

text of professional events. The present exploratory

study uses publicly available social media data of a

professional event to create an implicit relationship

and evaluate the tie strength.

3 RESEARCH METHOD AND

APPROACH

In this section, we present a case study where so-

cial media data was collected for professional event

CMAD 2016. Along with the social media data, a

questionnaire was given to event participants to cap-

ture the individual’s perception of their actual tie

strength. The following subsections provide details

about the case description; followed by details about

the data collection process, and the ﬁnal subsection

describes the different data analysis methods used in

this study.

3.1 Case Description

Our case study environment was community man-

agers’ online discussions in social media in connec-

tion to yearly- organized Community Manager Ap-

preciation Day (CMAD2016) event that took place on

January 25, 2016, in Jyv

askyl

a, Finland and had 270

event participants. The selection of case CMAD2016

was done because it satisﬁed the conditions suggested

by Yin (1994) for selecting a single-case design-based

case study. Case CMAD2016 was an extreme or

a unique case that was relevant for the overall goal

of this study which was to evaluate tie strength us-

ing publicly available social media data in a profes-

sional event. Firstly, CMAD2016 was a professional

event which has a majority of the event participants

belonging to the community of community managers

who can be considered as advanced lead users of so-

cial media and online community management ap-

proaches, with most of them being highly active in

social media (Aramo-Immonen et al., 2015, 2016).

Secondly, these event participants are not only active

on the social media in general but also use the social

media in the event CMAD2016 for various purposes

like networking and maintaining relationships (see

(Aramo-Immonen et al., 2015, 2016)). Thirdly, the

social media data related to event CMAD2016 is pub-

licly available which is essential to the main research

problem that this study addresses. Finally, based on

previous studies of community managers in Finland

(see (Aramo-Immonen et al., 2015, 2016)), we argue

that community managers communicate with each

other also between events, and have also participated

actively in planning the event, and assume that by col-

lecting data based on these community member’s dis-

cussions from Twitter and Facebook we can capture

sufﬁcient and representative amount of data to draw

conclusions.

To be usefully able to do tie strength related anal-

ysis using social media discussion data in the con-

text of professional events, we created a list of some

major preconditions to enable the overall analysis of

this study: 1) a reasonably large number of partici-

pants must be present and active in social media in

an event; 2) despite the geographic co-location in an

Identifying Different Types of Social Ties in Events from Publicly Available Social Media Data

179

event, participants should still use social media to es-

tablish new ties or strengthen existing ties; 3) because

tie strength must be deduced from discussions only,

the motives of discussions in social media should be

more than just information sharing, ranging to main-

taining existing ties and creating new ties; 4) the car-

ried out discussions in social media should reﬂect net-

works and ties to a useful and sufﬁcient extent, and

5) data related to tie strength dimensions and predic-

tors must be extractable to a useful extent from pub-

licly available social media data within an event. The

current literature ﬁnds support at least for precondi-

tions 2) (Zhang et al., 2016), as well as 3) and 4)

(Ahn and Park, 2015). Preconditions 1) and 5) can

be impacted by careful selection of case event to suit

the purpose. Though the above preconditions cannot

be extensively tested or proved within the limitations

of one individual case study, we also collected some

precondition- related data and demonstrate from the

collected and analyzed data that the above precondi-

tions were met to a useful extent.

3.2 Data Collection

Two different sources of data were used in this study.

One source was social media data (Facebook and

Twitter) while the other source was a questionnaire.

3.2.1 Social Media Data

The social media data for the event CMAD2016 was

collected from Twitter and Facebook. The detailed

corpus statistics for both Facebook and Twitter data

are given in Table 2 and Table 3 respectively.

Table 2: Facebook data corpus.

Content Attribute Value Actor Attribute Value

Time Period

Start: 2013-02-04

End : 2016-05-23

Total Actors 8798

Total Page Likes – Total Unique Actors 374

Posts 555 Unique Posters 81

Comments 2925 Unique Commenters 199

Comment Replies 149 Unique Comment Reply Actors 53

Likes on Posts 2529 Unique Wall Post Likers 327

Likes on Comments 2536 Unique Comment Likers 204

Likes on Comment Replies 104 Unique Comment Reply Likers 38

Full historic fetch of the two Facebook pages

(CMADFI 2014 & CMADFI 2015) from 01-01-2014

to 26-05-2016 was conducted using the Social Data

Analytics Tool (SODATO) (Hussain and Vatrapu,

2014; Hussain et al., 2014). SODATO enables the

systematic collection, storage, and retrieval of the

entire corpus of social data for Facebook walls and

groups. Twitter data was collected in two phases.

First, to list all tweets sent before, during, and after

CMAD2016, we accessed Flockler, a social media-

driven content management system that was used to

run the CMAD2016 website. Flockler provided a

web API to collect all tweets related to CMAD2016.

The Tweet Ids from Flockler data were used to access

the full set of Tweet data and metadata from Twitter

REST API using a tailored batch script. The batch

script exports tweet data in JSON for further process-

ing. For this study, the social media data from 1st

September 2015 to 30th April 2016 was used for per-

forming all the analysis.

Table 3: Twitter data corpus.

Content Attribute Value Actor Attribute Value

Time Period

Start: 2013-01-21

End : 2016-04-18

Total Users 12454

Total Tweets 12454 Total UniqueUsers 1651

Original Tweets 7568 Unique Original Tweet Users 858

Retweets 4886 Unique Retweet Users 1262

3.2.2 CMAD2016 Participant Questionnaire

Data

The second source of data was collected from the

event participants directly as this data provided us a

way to interpret the social media data against our the-

oretical framing. The questionnaire was developed by

adapting tie strength-scale by Petroczi et al. (2007)

based on the theoretical descriptions of strong ties by

Granovetter (1973). We wanted to capture the per-

ceptions of event participants on their strong ties and

possible weak ties from the event participants. We

excluded directly asking about weak ties as those are

higher in numbers (Granovetter, 1973) and are, there-

fore hard to recall by self- reported means. We devel-

oped the questionnaire shown in Table 4.

Questions 1 to 4 were framed to identify the strong

ties of the questionnaire respondents. Due to the prac-

tical problem of recalling names of questionnaire par-

ticipants, we limited the number of participant names

to ﬁve. Question 5 asked the participants to rate

the novelty of the information from three separate

groups of participants on a scale of 1-7. These three

groups were participants who questionnaire respon-

dent; knew well; met face to face but did not know

well; and not had face to face interaction with. Ques-

tion 5 was used to identify the different sources and

quality of the information in general. Question 6 fo-

cused on identifying novel information sources for in-

dividual questionnaire respondent. An online ques-

tionnaire link was shared to all the CMAD 2016 par-

ticipants through the CMAD Facebook group wall

and also by the ofﬁcial twitter handle of CMAD. The

survey was available in English and Finnish and was

based on the CMAD 2016 event only.

3.3 Data Processing and Analysis

To understand the temporal use of the social media

channels we performed temporal analysis of the so-

KMIS 2019 - 11th International Conference on Knowledge Management and Information Systems

180

Table 4: Questions from questionnaire.

Q1 Which 3 - 5 CMAD 2016 participants do you interact most frequently with ?

Q2 Which 3 - 5 CMAD 2016 participants would you most likely ask a personal favor from or return personal favor ?

Q3 Which 3 - 5 CMAD 2016 participants have you known the longest in professional context?

Q4 Which 3 -5 CMAD 2016 participants do you consider as your closest friend?

How novel (on an average) was the information, you received from the CMAD 2016 participants amongst the

following groups?

Q6 Which 3 - 5 CMAD 2016 participants do you consider as source of most novel information or ideas?

cial media data. Social network analysis was used to

create an implicit relationship network of event partic-

ipants based on the textual data of the publicly avail-

able social media data.

3.3.1 Temporal Analysis

We used data warehousing and on-line analyti-

cal processing (OLAP) technology using Microsoft

SQL Server database to conduct a temporal analy-

sis of Twitter and Facebook data. We designed a

multi-dimensional data model for Twitter and Face-

book data using interactions as numeric measures.

The interactions measure data was further processed

across several dimensions: temporal (daily, weekly,

monthly, and yearly), actions (post, comment, and

like in Facebook and tweets, retweets in Twitter) and

artifacts (posts, comments, tweets, and retweets).

3.3.2 Data Processing in Social Networks

Twitter and Facebook data, in general, allows straight-

forward analysis. In the case of Twitter, the used

REST API arranges the tweet data in a format that

is easy to process programmatically. This means that

the users (e.g. @menonkaran) and hashtags (e.g.

#cmadﬁ) are represented with an explicit syntax and

structure. In case of Facebook, posts, comments,

comment reply and likes were the entities used in the

analysis. A tailored Python script was implemented

to identify the above-mentioned entities in both Twit-

ter and Facebook data. The script further transformed

the reﬁned data into two networks:

• The ﬁrst network represents interconnections be-

tween people communicating over Twitter. More

speciﬁcally, with interconnections, we refer to

users mentioning each other in tweets through

comments and discussions.

• The second network shows interconnections be-

tween people communicating on Facebook. More

speciﬁcally, with interconnections, we refer to

users initiating Facebook posts, comments, and

comment replies as well as “Likes” to aforemen-

tioned Facebook entities.

The Python script uses NetworkX library (version

1.11) to construct the network and serialize it in Graph

Exchange XML Format or GEXF (version 1.2).

3.3.3 Social Network Analysis

Gephi, an interactive visualization and exploration

platform available in open source (Bastian et al.,

2009), was used to analyze and visualize the net-

works. Gephi was used to layout the networks, calcu-

late metrics for network nodes, analyze networks for

possible sub-networks (e.g. egocentric networks of

individual nodes) or clusters (Modularity Class met-

ric) calculated with Gephi’s implementation of the

community detection algorithm (Blondel et al., 2008)

and adjust the visual properties of the visualized net-

work according to the analysis. In this case, the eval-

uation of tie strength was done at the interpersonal

level (between the event participants) using commu-

nication frequency as a proxy for tie strength. The

weighted degree (sum of weighted indegree and out-

degree) and modularity class (clustering) were the

metrics that were of interest in the analysis. The lay-

out of the networks in this study was the result of a

force driven layout algorithm in which nodes repel

each other and the edges connecting the nodes act

as springs pulling the nodes back together (Blondel

et al., 2008). Hence, the nodes that are interconnected

will be placed close to each other.

4 FINDINGS

The descriptive analysis provided details about the

questionnaire and other results that support the pre-

conditions required to carry out this study. The tem-

poral analysis revealed the differences in the temporal

use of the two social media channels. The correla-

tional analysis helped in correlating the social media

data with the data gathered using the questionnaire.

4.1 Descriptive Analysis

In literature, Twitter use has been attributed to build-

ing or establishing new ties. This was found to be

true in case of events as well based on the Twit-

ter data about CMAD2016.Twitter was used not only

Identifying Different Types of Social Ties in Events from Publicly Available Social Media Data

181

Table 5: Correlation between strong ties based on questionnaire and social media data.

Q1 Q2 Q3 Q4

Total number of names received from 24 questionnaire respondents 94 79 77 52

Total number of names identiﬁed using the Twitter top 10 list based

on weighted degree of each of the 24 questionnaire respondent

29 26 28 15

Accuracy in terms of percentage of names identiﬁed from Twitter

30.9% 32.9% 36.4% 28.8%

Total number of names identiﬁed using the Facebook top 10 list based

on weighted degree of each of the 24 questionnaire respondent

20 20 16 12

Accuracy in terms of percentage of names identiﬁed from Facebook

21.3% 25.3% 20.8% 23.1%

Total number of names received from 24 questionnaire respondents

not found in Twitter data

8 6 6 6

Total number of names received from 24 questionnaire respondents

not found in Facebook data

30 27 29 17

for information sharing but also to develop new ties

and strengthen existing ties. Some examples of these

kinds of tweets found in the CMAD2016 Twitter data

are given below. These tweets were originally writ-

ten in Finnish and have been translated. “Today

Jyv

askyl

a, some and #cmadﬁ. If you have networked

communication and WordPress in mind, please con-

tact melink ” is an example of a tweet to establish new

tie. “Have a great #cmadﬁ-day in Jyv

askyl

a These

ladies won’t be able to make it today in person, but

we will be there in spirit and will follow live tweets!

#yhteis

omanagerit” is an example of a tweet related

to strengthening the existing ties.

From 270 CMAD2016 participants, 24 partici-

pants which included 16 female and 8 male belonging

to different Finnish cities and included both organiz-

ers (who were also participants) and event participants

answered the questionnaire. On Twitter 119 partic-

ipants had 10 or more conversations;134 participants

had 5 or more conversations, and 214 participants had

at least 1 conversation. On Facebook 30 participants

had 10 or more conversations; 49 participants had 5 or

more conversations, and 91 participants had at least

1 conversation. For question 5 in Table 4 related to

most novel information received by the questionnaire

respondents, the average rating (on a scale of 1 to 7)

for the the 3 different options were: 5.13 for had not

met face to face; 4.65 for had met face to face but did

not know well; and 4.00 for knew well.

4.2 Temporal Analysis of Social Media

Figure 1: Temporal distribution of CMAD’s Facebook

Data.

The social media activity of the event CMAD2016

was observed on both Facebook and Twitter from 1st

September 2015 to 30th April 2016. Fig. 1 shows

that there were more spikes in the number of com-

ments, posts and likes on CMAD2016 Facebook page

in weeks leading to the CMAD2016 event.

Figure 2: Temporal distribution of CMAD’s Twitter data.

Fig. 2 shows that there is only one large spike

in the number of tweets and retweets. This spike

in activity occurs during the week of the actual

CMAD2016 event.

4.3 Findings based on Questionnaire

Fig. 3 & 4 provides the visualization of the

CMAD2016 participant’s conversation on Facebook

and Twitter during the period of this study. The la-

beled nodes in the network graphs represent the ques-

tionnaire respondents (alphabetical letters A to X) and

their novel source of information as provided in the

response for question 6 in Table 4 (for example, re-

spondent is labeled as S while his/her novel informa-

tion sources are labeled as S1, S2, S3, and S4). The

interaction of the participants is made visible by con-

nections to other participants, more the interaction the

larger the size of the connection (line width in Fig. 3

& 4). The node color represents the cluster of nodes

in the network, according to a community-detection

algorithm that analyses the network to ﬁnd a group of

nodes that are particularly tightly interconnected.

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182

Figure 3: Force driven network of people(Facebook).

In the case of Twitter based network ( Fig. 4), 25

different clusters were identiﬁed. While in the case

of Facebook (Fig. 3) 4 different clusters were identi-

ﬁed. On comparing the response of question 6 with

the Twitter and Facebook networks (Fig. 3 & 4), it

was observed that in the case of Twitter in 80% of

cases, the novel information source of the respondent

were participants who were in a different cluster than

the respondent. In the case of Facebook in 80% of the

cases, the novel information sources of the respon-

dent were participants who were in the same cluster.

It was also observed that a large proportion of the re-

spondent’s novel information source was not present

in the Facebook data but was present in the Twitter

data.

Figure 4: Force driven network of people (Twitter).

From Fig. 3, it was observed that in the case of

Facebook there was one central node through which

most of the other nodes were connected. Also, there

were few nodes in Facebook data which were not at

all connected. These nodes are people who had initi-

ated a post on the CMAD2016 Facebook page but did

not receive any reply for the post. In the case of Twit-

ter (Fig. 4) there was no single central node through

which all the other nodes were connected.

A list of top ten participants based on highest

weighted degree using the egocentric network of each

questionnaire respondent was created for every re-

spondent. The reason for selecting the top ten par-

ticipants was to accommodate for the noise in the

data while creating the conversation based weighted

degree-based list. This noise in our case was related

to the conversations about general event announce-

ments, logistics queries, and queries to the organizers,

which may not be related to strengthening or building

of ties.

Figure 5: Calculation logic for individual percentage match.

Two separate egocentric networks were created

using Facebook data and Twitter data. These two top

ten name lists based on Facebook and Twitter data

were then compared with the responses.

The logic about how this comparison was done is

shown in Fig. 5. For example, if respondent N an-

swered question 1 with ﬁve participants name, then

these names were compared with the names from the

top ten list from Twitter and from Facebook. The

number of names identiﬁed from the top ten list of

individual respondent for both Twitter and Facebook

data was then recorded. This process was carried out

for all the responses of every respondent. This aggre-

gated result is presented in Table 5. In Table 5, Q1,

Q2, Q3 and Q4 refers to question 1 to 4 of the ques-

tionnaire (Table 4). In Table 5, the ﬁrst row of the

table provides the total number of names which were

received from the 24 respondents for each of the ques-

tions asked in the questionnaire. These names provide

the respondent’s perception of whom they consider as

their strong ties with respect to different dimensions

of tie strength. The second and third row provide the

total number of names that were identiﬁed from the

Twitter and Facebook data for each of the question.

The fourth and ﬁfth row provides the total number of

names from the respondents that were not found in

Identifying Different Types of Social Ties in Events from Publicly Available Social Media Data

183

the Twitter and Facebook data for each of the ques-

tionnaire question.

5 DISCUSSION AND

CONCLUSIONS

This exploratory study, unlike the previous studies

which used explicit online relationship data and /or

private social media data of study participants, uses

the publicly available social media data about an event

to derive implicit online relationships and evaluate tie

strength. Since the analysis of this study was built

on a relatively small number of questionnaire respon-

dents and one individual case study community, we

provide the following propositions that strive to en-

hance the current understanding of the research ques-

tion of this study.

Proposition 1: The Purpose and Pattern of use of

a Particular Social Media Channel in an Event Im-

pact how Accurately the Ties can be Identiﬁed.

In our study, the temporal distribution of the so-

cial media data (Fig. 1 & 2) showed that Twitter was

used mainly during the day of the event CMAD2016,

while Facebook was used more before the start of the

event. Also, from the force driven network of people

(Fig. 3 & 4) it can be observed there was one ap-

parent central node in case of Facebook while there

was no central node in case of Twitter. A possible

data-driven explanation for these observations would

be that Facebook Page might have been used for plan-

ning the event while Twitter was used only during the

event for maintaining ties or building new ones. This

preliminary explanation is also supported by the sam-

ple tweets that were provided in the descriptive analy-

sis of the tweets. Academically, this is novel because

when you rely on publicly available social media data,

then it is essential to understand the purpose and pat-

tern of the use of social media channels. Since in

this case, the content of the textual data is the only

source for deriving the implicit relationships. This

is because, in an event, different social media chan-

nels may be used for very different purposes. If this

aspect is not taken into consideration while deciding

on which social media channel should be selected for

evaluating tie strength, then selection of wrong so-

cial media channel would result in totally irrelevant

tie strength estimation.

Proposition 2: Structure of the Implicit Social

Network can Reveal the Possible Weak Ties, Provided

that the Selected Social Media Channel is used for

Maintaining and Building Ties.

Based on the response to questionnaire question

6 (Table 4) and the force driven network of people

based on tweets (Fig. 4), 80% of questionnaire re-

spondents belong to a different cluster than the peo-

ple who are their novel sources of information. Also,

these novel sources of information were connected

to a large number of event participants from differ-

ent clusters (Fig. 4). From the literature, we know

that weak ties are a source of novel information and

act as a bridge between diverse people (Granovetter,

1973). Hence, the above empirical ﬁndings together

with existing literature, provide support to the propo-

sition 2 statement. This proposition is academically

novel because previous studies have only used explicit

online relationship data from social media to create a

relationship network which was then used to identify

different ties (e.g. Backstrom and Kleinberg (2013)).

However, in this study only implicit relationships de-

rived from the communication of the event partici-

pants over social media was used. Such kind of data is

easily accessible in case of an event while explicit re-

lationship data is almost impossible to access in case

of an event. In practice validation of this proposition

in the future will lead to a new method for identify-

ing weak ties and would be highly relevant in building

collaboration tools like tie strength-based conference-

related social recommendation systems.

Proposition 3: Weighted Degree from Implicit Re-

lations from Social Media Data can Correlate with

Tie Strength, Especially the Strong Ties, Provided that

the Selected Social Media Channel is used for Main-

taining and Building Relationships.

In our study, we found preliminary evidence to

support this proposition. From Table 5 we were able

to identify questionnaire participants perceived strong

ties with an accuracy of about 30% in case of Twitter

data and about 20% in case of Facebook data. This ac-

curacy in predicting the perceived strong ties is good

because the identiﬁcation of the strong ties was done

only using the content of the social media. No other

explicit relationship data from the social media was

used in order to either identify the existence of the tie

nor for the speciﬁc identiﬁcation of strong ties. Only

the network parameter of weighted degree calculated

from the implicit network derived from the content of

the social media was used. The result of this analysis

is given in Table 5. This provides preliminary empir-

ical evidence for this proposition. Academically this

proposition is novel because the existing related stud-

ies (e.g. Ahn and Park (2015); Gilbert and Karahalios

(2009); Aral and Walker (2014)) have used measures

which are either based on the explicit online relation-

ships and/or private social media data. However, in

this study, the measure used for tie strength evalu-

ation was based on publicly available social media

data. This aspect is of practical relevance because, in

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184

most events, it is very difﬁcult to access the explicit

online relationship data or private data of participants

from social media. However, it is relatively easy to

collect data like actual textual content (e.g. Tweets

in case of Twitter, Text from open Facebook pages in

case of Facebook) related to the event. Hence, mea-

sures, which can evaluate tie strength from this kind

of social media data, will be useful while developing

tie strength-based conference recommender systems.

5.1 Managerial Implications

Based on the above propositions, the following man-

agerial implications should be considered. First, it is

essential to identify the purpose and pattern of use

of the social media channel in an event before using

it for tie strength calculation. Second, the structure

identiﬁed from the social media content (i.e. implicit

network) can be used to identify the event partici-

pants who connect the different discussion topic clus-

ters, can thus be considered as potential weak ties.

This implication would be relevant to consider for the

event organizers and also the conference recommen-

dation system designers as it will help the organizers

to identify the most diverse and networked event par-

ticipants. Finally, event organizers should consider

using some kind of standardization of social media

keywords for different discussion topics (e.g. Use of

certain # for speciﬁc topics) across different social

media channels. This will be useful for the event orga-

nizers to ﬁnd the most relevant topics for the event in

general. For the event participants, this will be help-

ful to ﬁnd the other potential event participants to net-

work or collaborate with.

5.2 Limitations

The study presented in this paper has certain limita-

tions. First, in this study, we studied only some poten-

tial approaches related to the calculating tie strength.

Second, due to the limited number of respondents

in our questionnaire used to conﬁrming the evalu-

ated ties and tie strength from social media data; we

were unable to draw any statistically signiﬁcant re-

sults. The current study is based on a single case-

based case study; thus, the results, in general cannot

be directly generalized to apply to all other confer-

ences and were presented as propositions.

5.3 Future Work

This study leaves room for future studies in many ar-

eas. First, all propositions of this study should be

tested and validated in future studies and in differ-

ent types of events, to allow further generalization.

Second, there are many dimensions and measures for

tie strength, in future studies, we will use more mea-

sures to evaluate tie strength in an event context. Fi-

nally, incorporating big social data (e.g. large col-

lection of Twitter data and public Facebook walls

of events) with other data sources like bibliographic

data, location data may enable developing automated

tie strength evaluation methods in case of events.

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