Synchronization of memories from dyads to networks Bottom-up approaches to the formation of collective memories investigate how cognitive-level phenomena allow for people to synchronize their memories following conversational remembering. Due to the malleability of human memory, talking with one another about the past results in memory changes that increase the similarity between the interactional partners' memories When these dyadic interactions occur in a social network, one can understand how large communities converge on a similar memory of the past. Research on larger interactions show that collective memory in larger social networks can emerge due to cognitive mechanisms involved in small group interactions. Computational approaches to collective memory analysis With the ability of online data such as social media and social network data and developments in natural language processing as well as information retrieval it has become possible to study how online users refer to the past and what they focus at. In an early study in 2010 researchers extracted absolute year references from large amounts of news articles collected for queries denoting particular countries. This allowed to portray so-called memory curves that demonstrate which years are particularly strongly remembered in the context of different countries (commonly, exponential shape of memory curves with occasional peaks that relate to commemorating important past events) and how the attention to more distant years declines in news. Based on a topic modelling and analysis they then detected major topics portraying how particular years are remembered. Rather than news Wikipedia was also the target of analysis. Viewership statistics of Wikipedia articles on aircraft crashes were analyzed to study the relation between recent events and past events, particularly for understanding memory-triggering patterns. Other studies focused on the analysis of collective memory in social networks such as investigation of over 2 million tweets (both quantitively and qualitatively) that are related to history to uncover their characteristics and ways in which history-related content is disseminated in social networks. Hashtags, as well as tweets, can be classified into the following types: General History hashtags used in general to broadly identify history-related tweets that do not fall into any specific type (e.g., #history, #historyfacts). National or Regional History hashtags which relate to national or regional histories, for example, #ushistory or #canadianhistory including also past names of locations (e.g., #ancientgreece). Facet-focused History hashtags which relate to particular thematic facets of history (e.g.,#sporthistory, #arthistory). General Commemoration hashtags that serve for commemorating or recalling