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Theme 1 – Learning and knowledge systems, education, extension and advisory services
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13th European IFSA Symposium, 1-5 July 2018, Chania (Greece)
An Investigation of Social Media’s Roles in Knowledge
Exchange by Farmers.
Tom Phillipsa, Laurens Klerkxb, Marie McEnteec.
aRural Innovation Research Group, Faculty of Veterinary and Agricultural Science, University of
Melbourne, (Australia), tom.phillips@unimelb.edu.au
bKnowledge, Technology and Innovation Group, Wageningen University (Netherlands),
Laurens.Klerkx@wur.nl
cSchool of Environment, University of Auckland (New Zealand), m.mcentee@auckland.ac.nz
Abstract: Social media (SM) such as Twitter and Facebook are new communication tools for rural
communities, and SM has enabled the creation of rural social networks. Increased use by farmers of
‘mobile digital devices’ and better rural access to broadband services have enhanced so that SM is
being used to support farming decisions.
However, in depth studies on how SM is used for knowledge sharing amongst farmers and the role of
rural professionals (e.g. advisors) in this space is an emergent field with limited literature. There is a
need to understand more about the roles SM play in farmer decision-making and agricultural innovation
more broadly. Is farmer participation oriented to strategic, tactical or operational farm decision making?
Little is known about differences in participation between farmers and rural professionals or about
learning processes and knowledge creation in these virtual spaces. Does SM create spaces where
participants engage on an equitable, trust forming and self-directed basis? What is the composition and
global reach of these media networks? How rapidly and flexibly do they form, disband and reconfigure?
To answer these questions, research methods used included ‘Twitter Scraping software’ and tools such
as ‘Twitonomy’ to mine data off selected Twitter accounts and farmer forums. Also, online pasture based
dairy farmer groups were examined in the Facebook study. Preliminary results suggest that Social Media
platforms can play a significant role in agricultural knowledge exchange practices. Farmers are building
new global networks through SM, willing to collaborate in social learning processes that are creating
change and shared cognitive meanings. A concept of farmer initiated ‘shared knowledge’ is emerging
from these online discussions. Posting questions sets the agenda, and farmers are sharing information,
providing validation and support for decision making.
Keywords: Social Media, Farmers, Rural Professionals, Opinion Leaders, Knowledge Exchanges,
Engagement, Online Communities.
Introduction
In recent years, there has been increasing attention to the role of ICTs, and related to that
softwares and internet based applications, as tools to support decision making, learning and
innovation in agriculture (Aker, 2011; Ballantyne, 2009; Poppe et al., 2013; Shanthy and
Thiagarajan, 2011; Sulaiman V. et al., 2012). However, only recently studies in the field of
learning and innovation in agriculture have started to include social media, a phenomenon
which has emerged with the progress towards Web 2.0 technologies and the rise of internet
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13th European IFSA Symposium, 1-5 July 2018, Chania (Greece)
enabled mobile phones (Cerkenková et al., 2011; Chowdhury and Hambly Odame, 2013;
Jespersen et al., 2014; Materia et al., 2014; Poppe et al., 2013; Rhoades and Aue, 2010).
Social media are a broad term to comprising different forms, but most dominant are social
networks like Facebook, LinkedIn, micro-blogging services like Twitter, and video and image
sharing platforms such as YouTube and Vimeo (for an exhaustive overview, see (Chowdhury
and Hambly Odame, 2013; Murthy, 2012).
Facebook was established in 2004 as a social networking site and Twitter began in 2006 as a
‘micro-blogging’ platform. It is estimated that 79% of Australians used Facebook and of that
49% use it daily (Sensis Report, 2017). There are now many social media sites on the internet
that all connect people to a personalised community in some way e.g. on Twitter the use of a
# (hashtag) allows members of a community to share in the conversation. The World
Economic Forum (August 2017) estimated that in an ‘internet minute’, there would be 900,000
Facebook logins (cf. 701,389 in 2016) and 452,000 tweets sent (cf. 347,222 in 2016). In the
past year Facebook logins grew by 28% and Twitter traffic by 30%. The growth in the personal
use of social media has been extraordinary. Facebook now has more than two billion monthly
users, SM is also being increasingly used for knowledge sharing amongst farmers and rural
professionals (e.g. advisors).
While there is an increasingly growing body of literature on people’s use in general of social
media, there is very limited literature available about how farmers and rural professionals are
using the numerous platforms (except Kaushik et al., 2018). There is, therefore, a need to
understand more about the roles SM play in farmer decision-making and agricultural innovation
more broadly.
To address this limited scholarship around farmers and rural professionals use of social media,
this paper analyses Facebook conversations from two pasture based dairy farmer Facebook
groups, and one series of twitter activity and then explores how these contribute to farmers,
decision-making, and knowledge sharing and (co)creation. The paper begins with an
exploration of the literature on farmer learning in networks including online networks. The
paper then moves to the results section which is divided into two parts. Part A contains analysis
of three Facebook groups. The first two took place in May 2014 with two separate ‘closed’
dairy farmer pasture groups, while the third took place in one of these groups in 2017. Part B
compares farmer and rural professionals use of twitter by examining the Twitter activity of 48
New Zealand ‘experienced’ agricultural Twitter account holders who posted 23765 tweets over
a 5-month period. The paper presents a discussion that explores how social media contributes
to farmer knowledge sharing and the potential this offers for agricultural innovation
Learning in farming networks - a literature review
The term ‘social media’ denotes highly interactive internet platforms via which individuals and
communities share, co-create, discuss and modify user-generated content which is media rich
(Kaplan and Haenlein, 2010; Piller, Vossen and Ihl, 2012). These online communities (OCs)
are open collectives of dispersed individuals and members with weak ties, who are not
necessarily known or identifiable but who share common interests (Sproull and Arriaga, 2007).
Furthermore, social network sites are “web-based services that allow individuals to (1)
construct a public or semi-public profile within a bounded system, (2) articulate a list of other
users with whom they share a connection, and (3) view and traverse their list of connections
and those made by others within the system. The nature and nomenclature of these
connections may vary from site to site.” (Boyd and Ellison, 2007:211).
Farms are part of a complex social, cultural and environmental ecosystem. Farmers are not
isolated individuals they are part of many social networks. Farmers are not passive receivers
of information, rather they build support networks for their constructs of reality (Kelly 1955,
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13th European IFSA Symposium, 1-5 July 2018, Chania (Greece)
Bannister and Fransella, 1971) in a world of rapidly increasing uncertainty and variability.
Farmers increase their farm business resilience (Shadbolt et al., 2013) by using ‘buffer
capacity’ (make the existing systems stronger), ‘adaptive capacity’ (make small changes to
existing systems), and ‘transformability’ (create completely new systems by making radical
changes) to cope with this volatility.
Their perceptions of what is ‘true’, what they can aspire to and what they are able to do, are
influenced by their daily routines, what has happened in the past and the feedback they receive
(Leeuwis, 2004). The nature of the strategic, tactical and daily decision-making (Shadbolt and
Martin, 2005) is heavily influenced by a historically grown farming system and the body of
knowledge that has evolved over time, plus their role on the farm. Prior to 1989 and the internet,
farmers lived in small sometimes isolated rural communities. Most neighbours were farmers
often farming in a very similar manner. Farmers and their families were regarded as mostly
self-sufficient, working with neighbours at seasonal peaks and socialising with the same
families. These rural communities had and still have tight social network ties.
So why do farmers join these networks. In theoretical terms, value creation can be
conceptualised as the formation of social capital. Bourdieu and Wacquant (1992: 14) define
social capital as “the sum of the resources an individual ‘accrues’ on the basis of belonging to
‘durable networks … of mutual acquaintance and recognition.” The concept has a varied and
influential history, including in online analysis. Putnam (2002) made an influential distinction
between the two types of social capital. Bonding capital is the value associated with networking
between homogeneous groups of people held together by strong ties. Bridging capital on the
other hand is the value associated with networking between heterogeneous people who are
weakly tied together. Bonding occurs when people socialize with like-minded people, bridging
capital occurs when people socialize with people not like them.
The principle of homophily (Bontcheva and Rout, 2014) says that people associate with other
groups of people who are most like themselves. Farmers bond with other farmers, who are
their main source of farm management information, despite the availability of agricultural
research, extension services and agricultural media (Phillips, 1985; Parnell et al., 2006; Barr,
2011, Evans et al., 2017). Social networks have been recognised as influencing an individual
farmer’s decision-making (Phillips, 1982; Phillips, 1985) and self-directed learning projects
(Tough, 1978).
Figure 1. A model – The learner’s social environment and learning pathway (Phillips, 1985).
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13th European IFSA Symposium, 1-5 July 2018, Chania (Greece)
Phillips (1985) developed a model to describe the farmer’s social network and how networks
were used to transform information to actionable knowledge and decision--making on farm.
Farmers’ trust in individuals in their network influenced the level of support they received from
those individuals. Phillips found acquaintances were used not only as a source of information
but importantly to validate information received, while intimates played a crucial support role
for the primary decision-maker. Many ‘information providers’ including extension workers had
only minor roles in the farmer’s decision making and learning. Fisher (2013) described the role
of social capital and trust in transforming information into usable knowledge.
The term ‘situated learning’ and the concept of ‘communities of practice’ (Lave and Wenger,
1990, Wenger 2000,) evolved to see knowledge being attained not purely from the individual
accumulation of information but as an act of social participation (Bandura, 1989). Collective
learning and a shared competence emerged from groups who met and connected regularly.
Communities of practice ask questions, request information, seek experience and problem
solve within their domain. The collective knowledge is a critical asset of the network and relies
heavily on the experience or tacit knowledge of members. Exposure to tacit knowledge, which
“maybe “harvested through storytelling, interviews, communities of practice and social
networks” (Evans et al., 2017:3) enables the construction of actionable knowledge.
A number of research papers about the nature of agricultural knowledge exchanges and farmer
learning have emerged from a major ‘learning project’ (Managing perennial summer forages)
conducted by a multi-disciplinary team at Massey University in New Zealand (Sewell et al.,
2014a, Sewell et al., 2014b, Wood et al., 2014, Drysdale et al., 2017, Henwood et al., 2017,
Sewell et al., 2017). Collectively these highlight the failure of the traditional linear extension
model. Their work focuses on the processes of interactive learning, the importance of
belonging to a learning network, validation of learning and farmers’ self-efficacy. Self-efficacy
(Bandura, 1997) is a self-belief and confidence in one’s own ability and has been suggested
as a factor in decision-making (Drysdale et al., 2017), change management and social
networking and learning. This sociocultural approach to farmer learning (Sewell et al., 2017)
requires consideration of the networks, interpersonal skills and personal factors. One might
judge the effectiveness of a group, network or online community Woolley et al., (2015) by the
structures, processes and norms (Group up processes) or the group composition e.g. gender
and diversity (Bottom up processes).
Social media is now enabling people to participate in knowledge networks online. “Online
Communities (OC) are a virtual organisational form in which knowledge collaboration can
occur (often among people not known to each other) in unparalleled scale and scope. The
fluidity engenders a dynamic flow of resources in and out of the community and affords
collaboration. Online communities have a world-view that the collective intelligence is very
important. This is not dissimilar to peoples of ‘First Nations’ (Durie, 2005, Tapsell and Woods,
2008, Nicolson et al., 2012; Phillips, 2016) where traditional indigenous communities hold
collectivist principles and social values that value the wellbeing of the community above that
of the individual. “The fluctuation in tensions creates opportunities of working together” (Faraj
et al., 2011:1224). Much of the knowledge collaboration appears to take place in the absence
of existing social relationships. A growing ‘sense of belonging’ to a learning or problem–solving
community (Sewell et al., 2017) might override the importance of trust in face to face
engagement. Building collective knowledge capacity becomes ‘embedded’ (Sewell et al.,
2014) in participants’ social interaction. Furthermore, there is a substantial move away from
individual information centric thinking to community centric and ‘collective knowledge’.
People use social networking sites to share knowledge in online conversations or posts. Users
post text, photos, videos, links and icons to make their knowledge publicly visible or within
gated or restricted networks. These vary according to the social media platform. The posting
of knowledge is effectively for the ‘collective good’ of the social network, where recipients are
free to interpret, modify and use the knowledge. Majchrzak et al. (2013:39) describes four ways
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13th European IFSA Symposium, 1-5 July 2018, Chania (Greece)
people engage in online media: metavoicing, triggered attending, network-informed
associating and generative role-taking. While people’s comments and replies are reasonably
straightforward, ‘likes’, ‘shares’, acronyms, abbreviations and neologisms (Reed, 2014) are
more complex to interpret and often have multiple meanings. Conversations are recognised
as key building blocks that enhance interactive learning and the knowledge of the members
within the online community (Raaijmakers et al., 2008, Woolley et al., 2010, Woolley et al.,
2015). Conversations involve exchanges of opinion, or ‘constructs’ held by participants (Kelly,
1955) that they believe to be true. The resulting learning that may occur from the exchange of
“personally relevant and viable meanings” (Thomas and Harri-Augstein, 1976:2) may well
mean that individual’s constructs are changed.
Results: Part A - Analysis of Dairy Farmers’ use of Facebook Groups
The way in which 2100 dairy farmers, including farm staff and rural professionals were using
Facebook was investigated during May 2014 by analysing two different ‘closed secret’
Facebook groups (see Table 1), where participation required request and then administrator
acceptance into the group and conversations were held among ‘members’ only in what could
be termed a ‘gated’ community. The two groups were selected as the authors had unique
access. These online communities of pasture based dairy farmers have formed for the
purposes of discussing ‘Farm Management’ decision making (referred to as ‘conversations’).
The groups are not unique but data on similar groups has not been recorded.
Group A had 1400 members (established 2011) and was administered by a dairy farmer.
Group B (2008), had 700 international members from 12 countries and was established by a
pasture based dairy farm consultant.
The role of the group administrator is integral to the membership, group rules of engagement
and the social nature of the group. Both group administrators initiated 16% of the Facebook
conversations during the 2014 research. Group A’s administrator was a very active participant
in most conversations.
Facebook ‘knowledge exchanges’ were categorised into either ‘Conversations’, ‘Notices
(curated material) or advertisements’ (typically no comment responses to a post but possibly
‘likes’). Group A’s administrator actively discouraged ‘Job and Livestock sale’ notices whereas
they were encouraged in Group B.
Table 1. Comparison of Group A and B’s use of Facebook.
Group A
Group B
Totals
Averages
Totals
Averages
Total number of Farm
Management
conversations (only)
47
31
(Social not
included)
Total number of posts
1354
28
191
6
Number of posts asking
questions
138
3
30
1
Number of posts replying
to questions
991
20
134
4
Validation of comments
225
5
27
1
Likes
1456
31
222
7
Photos
2
3
Links
4
10
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13th European IFSA Symposium, 1-5 July 2018, Chania (Greece)
Research days of
Facebook activity
5
70
New conversations per
day per group
10
>1
Total conversation time
(average time per
discussion in hours)
549
12
2195
71
100 Facebook ‘knowledge exchanges’ were analysed, 50 for each group. Group A conducted
50 conversations in 5 days (10 new engagements per day) whereas Group B took 70 days to
complete 50 conversations. The average duration of a conversation was 35 hours (Group A
nearly 12 hours average, Group B 70+ hours).100 ‘knowledge exchanges’ was judged to
representative of the farmer Facebook group activity. Group A was more active than Group B.
Table 2. Analysis of the main conversations according to conversation topics for Group A and B
Conversation Topics
Conversations Scores
Animal
Health
Employment
Farm
Business
Nutrition
Pastures
Group A
Total number of
conversations
2
14
7
2
2
Percentage of topics
4.3
29.8
14.9
4.3
4.3
Total number of posts
45
704
195
20
38
Percentage of posts
3.3
52.0
14.4
1.5
2.8
Group B
Total number of
conversations
1
5
5
5
3
Percentage of topics
3.2
16.1
16.1
16.1
9.7
Total number of posts
7
14
29
35
36
Percentage of topics
3.7
7.3
15.2
18.3
18.8
Combined Group
Total number of
conversations
3
19
12
7
5
Percentage of topics
3.8
24.4
15.4
9
6.4
Total Number of posts
52
718
224
55
74
Percentage of posts
3.4
46.5
14.5
3.6
4.8
All conversations were categorised into 16 different subject groupings (Table 2). The topics
discussed were seasonally relevant to the initiator of the ‘conversation’. In Group A,
conversations were dominated by employment related topics (30%), social (21%) and farm
business management 15%.
In Group B the nature of the conversation topics was different. There were very few social
conversation topics and a greater spread of farm topics, employment (16%), farm business
management (16%), dairy cow nutrition (16%), milking 13% and pastures (10%).
Advertisements were predominantly job and livestock sales related. Notices (curated material)
covered a wide range of websites/links of interest to farmer group members.
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13th European IFSA Symposium, 1-5 July 2018, Chania (Greece)
‘Farm Management conversations’ that were analysed contained 1545 comment responses
and 1724 ‘likes’ i.e. 42 responses per conversation. The average conversation had 20
comment responses (Table 3). The groups had different levels of engagement activity. Group
A (28 comments plus 31 likes per conversation) compared to Group B (6 comments plus 7
likes per conversation). There was a range of engagement from a large employment related
conversation (112 comments plus 141 likes) to the smallest being (2 comments of clarification
in a livestock for sale conversation).
Table 3. Summary of both groups 2014 Farm Management conversations.
Conversations
Total
Total no. conversations
100
Total no... of responses
1545
No. Posts asking
questions
168
No. of posts replying
1125
Validation
252
Likes
1724
Research time days
6
New conversations/ day /
group
21
Total conversation time
hours
2743
The 1545 comment responses were analysed as ‘Asking further Questions’ (11%), ‘replying
and providing additional information’ often tacit knowledge (73%) and 16% were ‘Validation
responses of either the original post or to subsequent responses’. The ‘likes’ are also a form
of validation. So, if the ‘likes’ are added to the ‘validation comments’ the total forms of validation
responses (1976) becomes very significant.
Table 4. Differences between the two (closed) dairy Facebook groups
Group A
Group B
Group members
1400
700
Age of group
3 years
6 years
Location
Mainly NZ
International
Time to record 50
engagements
5 days
70 days
Average New
Conversations per day
9+
<1 per day
Average engagement per
conversation
28
comments
plus 31
likes
6 comments
plus 7 likes
Number of different
people initiating
conversations
38
29
Facilitation / Administrator
started conversations
8
8
Part A, 2017 analysis of Group B
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13th European IFSA Symposium, 1-5 July 2018, Chania (Greece)
In 2017, a further analysis of Group B’s Facebook group was undertaken as this international
pasture based dairy farmer group has evolved into a larger more active online community
group. What had changed to the group engagement and the knowledge exchanges?
Membership in December 2017 was 1208 compared to 700 in 2014 (+508) with 949 males
and 259 females (78% male). The group members come from 15 different countries with most
living in the UK (68%). Of the non-UK members there are participants from New Zealand
(13.2%), Ireland (9.2%), Australia (4.6%) and France (3.4%) with small numbers from
Indonesia, Netherlands, Belgium, Argentina, Norway, Saudi Arabia, Switzerland, Italy and
Latvia. Rural Professionals were 7% of the membership. This pasture based dairy group now
has a diverse membership.
During the 6 months to December 2017, there were 211 posts that initiated a conversation of
which 116 were advertisements placed by the members, 87 were Farm Management
conversations/questions (divided into Strategic, tactical and operational decision-making
topics). The remainder were an assortment of Administrator posts, photos of farm operations
or links (curated material) to other websites of possible interest to group member.
Table 5. Summary of Engagement Analysis 2017
Posts
Engagements
Engagements
per post
Advertisements
116
793
6.8
- Staff employment
40
397
10.0
- Livestock for sale
45
258
5
- Livestock wanted
13
67
5
Farm Management
87
2436
28
Strategic
47
1644
35
Tactical
28
445
15.9
Operational
12
151
12.6
Admin, Photos, Links
9
196
21.8
Average engagement per
post
212
3229
15.2
The number of new Farm Management questions (agenda setting posts) rose from less than
one per day (2014) to nearly 2 per day over the research period in 2017. In the Farm
Management ‘conversations’ the total engagement (comments/replies plus likes) increased
from 13 (in 2014) to 28 per post in 2017.
Table 6. Participation response of the group 2017
% of posts
% of
engagement
Advertisements
54.7
24.6
Farm Management
45.3
75.4
Type of Decision
% of Farm
Management
% of
engagement
Strategic
54
75.4
Tactical
32.2
19.9
Operational
13.8
6.7
Total Agenda Posts = 212
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13th European IFSA Symposium, 1-5 July 2018, Chania (Greece)
Although farmer advertisements for livestock and staff were 54.7% of the number of posts
during the 6 months in 2017 they only represented 24.6% of the engagement. Most of the
engagement and activity of the group discussions centred on Farm Management decision
making and knowledge exchanges. The dominance of strategic farm management decisions
reflects the membership being predominantly farm owners.
Table 7. People and Posts from Group B in 2017.
Post information
Total no. of different
people posting agenda
questions
121
No. of agenda posts
212
Members making 3+
agenda questions
18
The 18 members made
81
No. of members at
16.1.17
1208
No. of people posting
agenda
121
Representing % of
membership
10
Eighteen individual members asked 81 of the agenda questions which represents nearly 35%
of all Farm Management conversations during this research period. These 18 people (both
genders) appear to be younger members. Farm Management conversations were not initiated
by 1087 members (nearly 90% of the total group). This is not unusual in Social Media
knowledge exchanges. The rule of thumb is 10% engagement and 90% silent or passive
observers. The passive observers will include people who are keenly watching the discussion
but not being actively involved in that conversation, to those who are not regular users of
Facebook. It is difficult to assess their involvement nor should assumptions be made.
Table 8. Social Media Response Ratios.
Category
Number
Creator
1
Engagements
9
Silent Observers
90
Interpreting Farmers’ use of Facebook
Dairy farmers utilised Facebook ‘closed/secret’ groups for discussion to assist in farm
management decision- making. Only a small number of rural professionals joined the groups
largely as observers. The online groups act and behave in a very similar way to on-farm
discussion groups or groups of ‘like-minded’ farmers meeting to discuss on-farm business
issues. Online networks encourage self-managed groups, reducing barriers of time and
distance.
It is likely that belonging to a Facebook group is cost effective for the farmer participants, given
the time convenient and travel free nature of the involvement. For rural professionals Facebook
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conversations challenge typical forms of extension with the agenda being set by individual
members asking questions, not the administrator setting the agenda topics, however it
provides excellent opportunities for participatory engagement.
The closed groups which have high levels of privacy on Facebook, provide a secure
environment for open and frank conversations. Among participants there appears to be a
willingness to help each other in a supportive manner within the online community. “This is a
great way to meet like-minded farmers where you can be bluntly honest” quote from a UK
farmer in the group. Conversations typically begin with a question that sets the frame for the
resulting posts. Farmer members take each ‘agenda setting question’ and resulting
conversation on merit and try to contribute positively by adding information and solutions
gained from personal experience. The responses are based on farmers’ ‘tacit knowledge’
which provide information for the discussion. However, validation of knowledge and support
for on-farm decisions or changes in farm management thinking, modes of operation and
strategic direction, were also an important component of the knowledge exchanges.
Topics reflect the time of year and the seasonal cycle of tasks on a dairy farm for the farmer
who initiates the question/conversation thread. The nature of the comment responses, display
a high level of experience, knowledge and expertise. Opinions and observations are
challenged. The personal ‘storytelling’ is powerful, effective and appreciated by other group
members. The administrators do initiate new conversations but their main activity is
establishing community norms for the group.
Each group has a strong social identity created by the nature of the group diversity, community
norms and participants posts contribute in a supportive way. The online social behaviour
appears to reflect life in rural or learning communities, although each group is distinctly
different. Group A are mainly farm managers and sharefarmers from New Zealand whereas
Group B is international, gender diverse and mainly farm owners or key decision makers. The
time-zone differences within the international group (group B) act to increase the hours of
conversation and conversations that are ‘out of season’ and non-English speaking farmers
appear to have more limited engagement in conversations e.g. the French speakers tend to
be passive observers not often posting. While farming in a different country usually excludes
interest in buying or selling livestock or responding to social media livestock advertisements.
Farm staff advertisements are often written to attract potential staff from different countries.
Group B’s support for advertisements in their posts enables participants to advertise for staff
without paying commission or fees.
Possible reasons for Group B being a much more active group in 2017, include it being a
larger, more diverse community which is more experienced in using social media. The new
membership appears more active in seeking information and more willing to offer solutions
based on their tacit experience.
There appears to be a visible pathway from passive observer to active participant (Observation
– Comment - Curate and Create) in the way in which farmers communicate knowledge
exchanges on Social Media. All farmers are learning how to use these new SM tools to the
advantage of the farm business and their own personal learning. “Thank you for your
responses. I never expected to learn so much from joining this Facebook group. This is so
helpful for a beginner farmer like me.” Quote from a French farmer member in Group B.
Social Media knowledge exchange processes
Figure 2 reveals a simplified visual conceptualisation of how social media (SM) knowledge
exchange develops in online communities such as Facebook. Conversation threads start with
a question which effectively sets the agenda. The more detail and setting of the context or
background for the question, the more likely the question will get responses from the online
community.
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13th European IFSA Symposium, 1-5 July 2018, Chania (Greece)
Figure 2. Simplified visual conceptualisation of the social media knowledge exchange process amongst Facebook
users
Opinion Leaders (OL) provide detailed responses based on their tacit knowledge. These OL
posts that are often media rich with photos, figures, videos and links create a burst of SM
activity called a ‘Hotspot’. It is this ‘media richness’ and self-efficacy of individual posts that
identifies Opinion Leaders. Within the ‘Hotspot’ the OL plays an important role of providing
further information and answering questions. The conversation is quick, not a linear process
and can be ‘interrupted’ by a second or third OL. These OL comments in turn create their own
‘Hotspot’.
The following excerpt from the dairy farmers’ Facebook conversation saw hotspots of social
media activity develop around opinion leaders’ media-rich comments.
Conversation Original Question (UK):
I'd be interested to see photos and hear comments about newly sown permanent pasture.
What was in the seed mix? Has it been grazed? When was it sown? Do you know the cost per
hectare? Why are you doing it?”
Opinion Leader Comment (UK):
“£408/ha, sown in the autumn, sprayed with glyphosate left 2 weeks then subsoiled with a
sumo GLS, then left a further 4 weeks to avoid fruit fly, slurry applied over this period at
90m3/ha, ploughed with a 4-furrow plough with discs and furrow press, one pass with 3m
power Harrow/ Cambridge roller combination, drilled with 6m corn drill with pipes removed and
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13th European IFSA Symposium, 1-5 July 2018, Chania (Greece)
flat rolled. Usually go for a high sugar ley but thinking of changing due to cost. Reseed every
8 years as that is when we see performance in the pasture drop.” (6 photos included in post)
Opinion Leader comment (Germany):
“We under-seeded our barley/pea ‘Whole crop’ with 12kg/ha herbal ley this spring and the
sward is great. As we're organic, we just incorporated it into our weed control. We go through
our crops with a 6m Köckerling Striegel which has a pneumatic seed drill built on” (3 photos
included).
Opinion Leader comment (New Zealand):
“We have just sown 2 paddocks here in NZ. 2 paddocks apart. 1. Full cultivation- Sprayed with
glyphosate-ploughed-heavy rolled-cultivated twice then roller drilled with 22kg/ha Base-
tetraploid ryegrass + 3 kg white clover/ha. 2. Sprayed with glyphosate- direct drilled. 21kg/ha
trojan ryegrass + 4kg white clover. Our goal is to use all direct drilling on our property for
regrassing and just go through full cultivation if post fodder beet or the paddock is rough and
we want to smooth it out. Photos are from yesterday day 13 since drilling. Since sowing we
have had no rain though both are fully irrigated with a centre pivot. For us the big advantages
of direct drilling are the limited impact on the soils, it doesn't pull up all the stones!! Its
considerably cheaper and the paddock is returned to the rotation a lot quicker for grazing.” (2
photos included in post).
Conversations range from 2-150 hours and often end abruptly, either because the
conversation has run ‘its’ course or it is being pressured by the presence of new topics and
engagement in new conversations. There are rarely conclusions drawn nor summaries of the
complete conversation unless the original questioner does so. There is no facilitator nor
chairperson managing the discussion. Furthermore, opinion leaders in one conversation about
a topic are rarely OL in a different topic.
Conversations are spontaneous and unpredictable. Vigorous discussion (based on the number
of posts/day and the degree of media richness) is likely to encourage more posts. Facebook
archives the conversation which adds to the collective knowledge of the network or community.
The nature of most knowledge exchanges would suggest a strengthening of both buffer and
adaptive capacity (Shadbolt et al 2013) rather than evidence of transformability. The social
environment is supportive, either neutral or positive and encouraging of individuals ‘learning
efforts, providing evidence of ‘social bonding.
Conversations are much more complex than suggested by the above conceptualisation. The
main reason for this complexity is that not everyone is in the same room at the same time,
compared to a face to face meetings as detailed by Raaijmakers et al. (2008). Using the actual
time each post is logged on Facebook, with participants being to be identified in login order
and the time and date recorded, the complexity would become more apparent. To analyse the
conversation, posts could then be identified as questions, comments, requests, answers or
likes and the emotions (positive, negative or neutral) adapted from Raaijmakers et al. (2008).
This methodology for social media analysis is being explored further by the authors and may
include Twitter ‘knowledge exchanges’ which the authors believe could be similar.
Results: Part B - Comparing Rural Professionals and Farmers use of
Twitter.
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13th European IFSA Symposium, 1-5 July 2018, Chania (Greece)
The Twitter component of this social media study examined the Twitter activity of 48 New
Zealand agricultural Twitter accounts, posting 23765 tweets over a 5-month period (YTD
2014). These accounts were chosen because they were experienced Twitter users. The 24
farmers had posted in total 60,428 tweets (average 2518 per account) and the rural
professionals 40,174 tweets (average 1674 per account). The farmers had an average of 550
followers each and the rural professionals 484 followers per account. This research did not
examine either Twitter forums or the use of hashtags.
Twitter generates metrics that code the tweets according to an array of variables and we have
used Twitonomy to collect these scores. The general approach is aggregating Twitter metrics
to create composite variables that measure online interaction. This sort of approach is
frequently used, especially by the copious marketing research done on Twitter. For example,
a common way of measuring a tweeter’s market ‘engagement’ is to sum their replies, retweets
and mentions. It compounds what can be distinguished as the values of bridging (retweets)
and bonding (replies and mentions).
Twitter activity among the selected group of farmers and rural professionals in New Zealand
shows relationship building through active communal conversation. This is most evident in the
high proportion of replies as the means of communication, and suggests twitter use among
rural professionals and farmers is well evolved with open participation, collaboration
(retweeting) and fuller engagement (asking questions, providing answers/replies) dominating
one-way messaging (new/ original tweets) – see Figure 3.
Figure 3. Twitter activity among selected farmers and rural professionals in New Zealand
There were key differences in approach between rural professionals and farmers. Rural
professionals made greater use of retweeting, links and being retweeted, all forms of bridging
capital. Farmers were considerably more active ‘repliers’, mentions, being favourited, following
and being followed (bonding social capital). Initial findings suggest farmers used twitter more
conversationally (question and answer). Rural professionals’ used twitter to disseminate
information rather than as a platform for actively engaging personal responses. Distinctions
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13th European IFSA Symposium, 1-5 July 2018, Chania (Greece)
were evident among rural professionals and farmers in terms of impact (incidence of tweets
being retweeted – see figure 4) and content (inclusion of externally created content)
Figure 4. Comparison of tweets retweeted by farmers and rural professionals.
A low level of content being retweeted by other users may suggest a small, well defined
community with content being narrowly targeted at specific users (Figure 5). A low inclusion of
links has some correlation with the high proportion of activity generated through ‘replies’, rather
than new or retweeted material.
Figure 5. Comparison of farmers’ and rural professionals’ tweet types.
Dairy farmers used Twitter between 4am-10pm 7 days a week and sent 5-11 tweets per day,
whereas Rural Professionals only sent 1-3 tweets per day. Farmer’ Twitter users ask questions
and offer tacit knowledge in replies to help fellow farmers problem-solve. The social ties are
even weaker than the Facebook closed groups. By posing questions members set the agenda.
Conversation is very fast and can rapidly engage multiple players worldwide. Social media
connects farmers and rural professionals to enhance on-farm decisions.
Interpreting Farmer and Rural Professionals use of Twitter
Participants use Twitter to seek and share information and to gain social support through
expanded online networks. Qualitative analysis of tweets revealed they were mainly about
farming and personal experiences of the user. With no expectation of a response to their
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13th European IFSA Symposium, 1-5 July 2018, Chania (Greece)
tweets, users gauge audience reception and acceptance of their twitter streams by the number
of followers they have, the level of re-tweets their messages receive and how often tweets are
favourited by followers. Users analysed in this research, appeared to be acutely aware of their
audience even if there was little direct feedback from that audience. One New Zealand farmer
who had 999 followers clearly enjoyed others following his tweets and offered to reward his
1000th follower with a chocolate fish (an iconic kiwi sweet) as he had done for his 100th and
500th followers.
Tweets were frequently written as informal comments about life on the farm, and sometimes
included links to interesting media stories and websites. Pictures were also posted to share
with others about ‘life at the office’. Although Twitter was more commonly accessed by users
in this study on a computer, its accessibility via a smart phone enabled tweets to be posted
throughout the day. Dairy farmers who were active users would tweet from 4am till 10pm,
seven days a week with peak tweeting occurring after morning milking. Twitter enabled people
to stay connected according to their daily routine. One rural professional app developer
tweeted during the night as he worked and was quiet during the day when he slept. Social
media does not require real time audiences.
Farmers’ use of twitter displayed a visible evolutionary pathway (Observation, Comment,
Curate and Create):
1. Just Observation
2. Low Engagement (One Way Messaging)
3. Open Participation to Collaboration (retweeting)
4. Fuller Engagement (Creating Two Way Conversations)
Whereas rural professionals twitter usage is largely ‘Low Engagement’ with mostly one-way
messaging. This suggests a more linear and traditional ‘top down’ approach to ‘extension’
thinking and philosophies and does not appear to maximise the potential of social media as a
platform for collaboration or knowledge exchange. The farmers on the other hand have in
relatively short time moved from just observing to an effective use of the SM tools in Facebook
and Twitter, to create two-way conversations and fuller engagement with the communities they
have joined. This is a ‘network model’ of knowledge exchange as described by Jespersen et
al (2014).
Discussion
This work reveals that farmers are building new international networks on social media and
are willing to work together toward meaningful change. They are using social media in online
networks to advance their self-directed learning strategies, mostly with other farmers.
Facebook and Twitter conversations in this research showed that farmers used social media
to connect with other farmers and rural professionals (bonding and to a lesser extent bridging
capital.) Farming is noted for its social isolation caused by its geographical remoteness and
long working hours (Alston 2012). Social media provides channels for breaking down this
isolation by enabling rural actors to stay socially connected, while still being physically remote.
While Twitter and Facebook both facilitated social connectedness, they achieve this in different
ways.
Unlike Twitter, Facebook provided more scope for conversations, with an expectation from
users that posts would be answered. Rural users used Facebook to solve problems, gather
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13th European IFSA Symposium, 1-5 July 2018, Chania (Greece)
information and converse with virtual networks on topical and even controversial issues. Since
the platform’s accessibility was more suited for use on a computer, conversations typically
started in the evening once work on the farm had ceased for the day. Problem solving
discussions were largely designed to enhance on-farm decisions.
Like on-farm discussions, virtual problem-solving discussions, showed the constructed nature
of knowledge production. In the virtual world, as in real world discussions, knowledge that is
not readily available is developed and adapted ‘on the spot’ through interactions between
farmers, scientists and rural professionals (Leeuwis, 2004). This development is an important
source of knowledge exchange for innovation, and corroborates recent findings by (Kaushik et
al., 2018). In social media, however participants are often international. Local discussions
became global discussions in the virtual world. While networks were largely dominated by
farmers, rural professionals did join conversations. Members’ responses were shaped by their
personal characteristics such as the user’s experiences, status on the farm, age and gender.
In the virtual world, non-verbal cues such as body language and vocal variation that are so
critical for message understanding in face-to-face communication, appear to be replaced by
language embellishments. Texting language - LOL (laugh out loud) or exclamation marks (!!!!!)
are used to enrich comments in these virtual discussions. Female participants in the analysed
conversations were more likely to add these language embellishments to their comments. The
online community’s diversity and gender balance (Woolley 2015) is a key factor in the quality
of knowledge exchange.
Users in discussions, acted as a peer network consisting of weak ties. The importance of
social networks in fostering change in the agricultural sector is widely recognised (Phillips,
1985; Ridley, 2005; Kroma, 2006; Sligo and Massey, 2007). The development of weak ties in
networks is recognised as an important source of information for innovation development
(Gielen et al., 2003). Problem solving discussion groups in social media therefore have the
potential to provide a useful channel for fostering important weak ties that are deemed
necessary for innovation development.
Conclusion
This research has shown that farmers use social media for knowledge exchange to support
on-farm decisions. Knowledge that is not readily available is discussed, questioned and
validated within online communities and potentially could give rise to rural innovation. Farmers
using both Facebook groups and Twitter have mastered the skills of full social media
engagement and have embraced the concepts of community (a network model) and collective
knowledge. Whereas rural professionals are primarily using social media as a platform to
disseminate information and do not appear to be maximising the potential for social media to
engage in knowledge exchange. The nature of the social capital being employed by farmers
(bonding) is quite different to that of rural professionals (bridging).
The role of ‘Opinion Leaders’ (OLs) has emerged as being very important in creating ‘Hotspots’
of high activity in online conversations. OLs share media-rich tacit knowledge and if the
community is diverse, vigorous high-quality discussion can be created. The OLs demonstrate
high levels of farmer self-efficacy and trust. Although the connotation of trust appears to be
one of the distinguishing differences between social media activity and face to face farmer
problem solving. In social media much of the knowledge collaboration appears to take place
in the absence of existing social relationships. The initiating questions are random, chaotic and
unpredictable. However, the personal storytelling is powerful, colourful and direct.
More research is required to develop a diagnostic methodology to fully unravel the complexity
of online dialogue fragments and sequences and provide more empirical evidence that reveals
the nuances of dialogue as well as group dynamics that occur in social media knowledge
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exchanges. By doing this should reveal more deeply how social media activity and
communities contribute to agricultural innovation.
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Acknowledgements
The authors would like to acknowledge the inputs of P. Edmond, B.A. Wood and Lyndon S. Phillips.
